Neuroscience informatics最新文献_第2页

Neuroscience informatics

Pub Date : 2026-01-03 DOI: 10.1016/j.neuri.2025.100255

My N. Nguyen, Yoshiki Kubota, Akimasa Hirata

This study investigated age-related changes of brain fiber pathways from diffusion tensor imaging (DTI) tractograms with directional decomposition. Two hundred subjects were stratified into three age groups. Tractograms were generated at two levels: from individual DTI images (subject-level), and from group-averaged images (group-level). Fiber tracking was performed within the cerebral white matter, brainstem, thalamus, and cerebellum at both the levels. Each tractogram was decomposed into directional tracts. At the subject-level, original and decomposed tracts were used to quantify tract density and correlations with age. Tract density was highest in the thalamus and brainstem, while the cerebellum showed the greatest inter-subject variability. Tract count exhibited some significant correlations with age: in cerebral white matter, it decreased overall, especially along S-I and A-P directions; in thalamus, S-I and A-P tracts decreased, while L-R and mixed-direction tracts increased. The brainstem tracts demonstrated its overall stability during aging. At the group level, ∼60 % of brainstem tracts were oriented along the S–I direction, and ∼64 % of cerebellar tracts along the A–P direction. Notably, the posterolateral tracts of the cerebellum showed asymmetry, with the left side associated with visuospatial processing, containing fewer tracts than the right side associated with language pathways. These findings highlight region- and direction-specific changes with age, revealing structural patterns that are not captured by conventional scalar measures. They suggested candidate biomarkers for brain aging and provided useful references for longitudinal neuroimaging and brain stimulation studies, with potential applications in the early detection of neurodegeneration and optimization of stimulation strategies.

本研究利用定向分解扩散张量成像（DTI）图研究脑纤维通路的年龄相关性变化。200名受试者被分为三个年龄组。在两个层次上生成束状图：来自个体DTI图像（受试者水平）和来自组平均图像（组水平）。在脑白质、脑干、丘脑和小脑两个水平上进行纤维跟踪。每个束图被分解成方向束。在受试者水平上，使用原始和分解的束来量化束密度及其与年龄的相关性。丘脑和脑干的束密度最高，而小脑则表现出最大的主体间变异性。脑道数与年龄有显著的相关性：脑白质总体减少，尤其是沿S-I和A-P方向；丘脑S-I束和A-P束减少，L-R束和混合方向束增加。脑干束在衰老过程中表现出整体稳定性。在组水平上，约60%的脑干束沿S-I方向定向，约64%的小脑束沿A-P方向定向。值得注意的是，小脑的后外侧束表现出不对称，左侧与视觉空间处理有关，比右侧与语言通路有关的束少。这些发现突出了区域和方向随年龄的变化，揭示了传统标量测量无法捕获的结构模式。他们提出了脑老化的候选生物标志物，为纵向神经成像和脑刺激研究提供了有用的参考，在神经退行性疾病的早期检测和刺激策略的优化方面具有潜在的应用价值。

{"title":"Age-related changes in brain fiber pathways based on directional decomposition of DTI tractograms","authors":"My N. Nguyen, Yoshiki Kubota, Akimasa Hirata","doi":"10.1016/j.neuri.2025.100255","DOIUrl":"10.1016/j.neuri.2025.100255","url":null,"abstract":"<div><div>This study investigated age-related changes of brain fiber pathways from diffusion tensor imaging (DTI) tractograms with directional decomposition. Two hundred subjects were stratified into three age groups. Tractograms were generated at two levels: from individual DTI images (subject-level), and from group-averaged images (group-level). Fiber tracking was performed within the cerebral white matter, brainstem, thalamus, and cerebellum at both the levels. Each tractogram was decomposed into directional tracts. At the subject-level, original and decomposed tracts were used to quantify tract density and correlations with age. Tract density was highest in the thalamus and brainstem, while the cerebellum showed the greatest inter-subject variability. Tract count exhibited some significant correlations with age: in cerebral white matter, it decreased overall, especially along S-I and A-P directions; in thalamus, S-I and A-P tracts decreased, while L-R and mixed-direction tracts increased. The brainstem tracts demonstrated its overall stability during aging. At the group level, ∼60 % of brainstem tracts were oriented along the S–I direction, and ∼64 % of cerebellar tracts along the A–P direction. Notably, the posterolateral tracts of the cerebellum showed asymmetry, with the left side associated with visuospatial processing, containing fewer tracts than the right side associated with language pathways. These findings highlight region- and direction-specific changes with age, revealing structural patterns that are not captured by conventional scalar measures. They suggested candidate biomarkers for brain aging and provided useful references for longitudinal neuroimaging and brain stimulation studies, with potential applications in the early detection of neurodegeneration and optimization of stimulation strategies.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal dynamics of TMS-Evoked responses: A dual damped sine model analysis of cortical site and stimulation condition effects tms诱发反应的时空动态：皮质部位和刺激条件效应的双重阻尼正弦模型分析

Neuroscience informatics

Pub Date : 2025-12-25 DOI: 10.1016/j.neuri.2025.100254

Damián Jan

Background

Transcranial magnetic stimulation combined with EEG (TMS-EEG) provides a non-invasive window into cortical excitability and connectivity. However, interpreting TMS-evoked potentials (TEPs) remains challenging due to pervasive artifacts and the limited physiological interpretability of descriptive analytical approaches.

New method

We introduce the Dual Damped Sine (DDS) model, a parametric framework that decomposes TEPs into physiologically meaningful parameters: amplitudes (A₁, A₂), frequencies (f₁, f₂), and damping constants (γ₁, γ₂). We applied DDS to the publicly available OpenNeuro dataset ds001849 to assess its ability to capture site- and condition-specific cortical responses.

Results

DDS achieved excellent model fits (median R² ≈ 0.95; RMSE ≤10⁻⁶) and revealed significant site- and condition-specific differences in the early TEP window (15–80 ms). Active TMS produced larger amplitudes and stronger damping, particularly at DLPFC, with frequencies constrained to physiological bands. These findings are consistent with previous evidence that early TEP components reflect site-specific cortical activation (Siebner et al., 2019; Freedberg et al., 2020).

Comparison with existing methods:While traditional similarity metrics quantify global waveform differences, DDS provides mechanistic interpretation of TEP dynamics through its parametric decomposition. The model captures how cortical responses evolve in time, offering insights into excitatory-inhibitory dynamics.

Conclusions

DDS represents a novel analytical approach that not only confirms established findings about early TEP specificity but also provides physiologically interpretable parameters describing cortical response dynamics. This parametric framework advances TMS-EEG methodology by bridging the gap between waveform analysis and neurophysiological interpretation.

经颅磁刺激联合脑电图（TMS-EEG）提供了一个研究皮层兴奋性和连通性的非侵入性窗口。然而，由于普遍存在的人工产物和描述性分析方法的有限生理可解释性，解释tms诱发电位（TEPs）仍然具有挑战性。我们引入了双阻尼正弦（DDS）模型，这是一个参数框架，将tep分解为有生理意义的参数：振幅（A1, A2），频率（f1, f2）和阻尼常数（γ1, γ2）。我们将DDS应用于公开可用的OpenNeuro数据集ds001849，以评估其捕获部位和条件特异性皮层反应的能力。结果dds获得了极好的模型拟合（中位数R2≈0.95;RMSE≤10−6），并在早期TEP窗口（15-80 ms）显示出显著的部位和条件特异性差异。主动经颅磁刺激产生更大的振幅和更强的阻尼，特别是在DLPFC，频率限制在生理波段。这些发现与之前的证据一致，即早期TEP成分反映了部位特异性皮层激活（Siebner et al., 2019; Freedberg et al., 2020）。与现有方法的比较：传统的相似性度量量化了全局波形差异，而DDS通过参数分解提供了TEP动态的机制解释。该模型捕捉了皮层反应如何随时间演变，为兴奋-抑制动力学提供了见解。结论sdds代表了一种新的分析方法，不仅证实了早期TEP特异性的既定发现，而且提供了描述皮质反应动力学的生理可解释参数。该参数框架通过弥合波形分析和神经生理解释之间的差距，推进了TMS-EEG方法。

{"title":"Spatiotemporal dynamics of TMS-Evoked responses: A dual damped sine model analysis of cortical site and stimulation condition effects","authors":"Damián Jan","doi":"10.1016/j.neuri.2025.100254","DOIUrl":"10.1016/j.neuri.2025.100254","url":null,"abstract":"<div><h3>Background</h3><div>Transcranial magnetic stimulation combined with EEG (TMS-EEG) provides a non-invasive window into cortical excitability and connectivity. However, interpreting TMS-evoked potentials (TEPs) remains challenging due to pervasive artifacts and the limited physiological interpretability of descriptive analytical approaches.</div></div><div><h3>New method</h3><div>We introduce the Dual Damped Sine (DDS) model, a parametric framework that decomposes TEPs into physiologically meaningful parameters: amplitudes (A<sub>1</sub>, A<sub>2</sub>), frequencies (f<sub>1</sub>, f<sub>2</sub>), and damping constants (γ<sub>1</sub>, γ<sub>2</sub>). We applied DDS to the publicly available OpenNeuro dataset ds001849 to assess its ability to capture site- and condition-specific cortical responses.</div></div><div><h3>Results</h3><div>DDS achieved excellent model fits (median R<sup>2</sup> ≈ 0.95; RMSE ≤10<sup>−6</sup>) and revealed significant site- and condition-specific differences in the early TEP window (15–80 ms). Active TMS produced larger amplitudes and stronger damping, particularly at DLPFC, with frequencies constrained to physiological bands. These findings are consistent with previous evidence that early TEP components reflect site-specific cortical activation (Siebner et al., 2019; Freedberg et al., 2020).</div><div><strong>Comparison with existing methods</strong>:While traditional similarity metrics quantify global waveform differences, DDS provides mechanistic interpretation of TEP dynamics through its parametric decomposition. The model captures how cortical responses evolve in time, offering insights into excitatory-inhibitory dynamics.</div></div><div><h3>Conclusions</h3><div>DDS represents a novel analytical approach that not only confirms established findings about early TEP specificity but also provides physiologically interpretable parameters describing cortical response dynamics. This parametric framework advances TMS-EEG methodology by bridging the gap between waveform analysis and neurophysiological interpretation.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NeuroFusion: A forensic enriched ensemble framework for cerebellum disease classification 神经融合：小脑疾病分类的法医综合框架

Neuroscience informatics

Pub Date : 2025-12-23 DOI: 10.1016/j.neuri.2025.100251

Abu Hanzala , Md Sajjad , Tanjila Akter , Harpreet Kaur , Md Sadekur Rahman

Accurate and timely classification of cerebellar diseases is crucial for effective diagnostic, yet it remains challenging due to the inherent heterogeneity of these disorders and the subtlety of their neuroimaging manifestations. This study investigated a novel multi-stage ensemble framework integrating SE blocks and segmentation-assisted augmentation tailored for limited cerebellum disease MRI data. Dataset included 3296 MRI scans from four classes and we divided dataset into three parts: training, testing, and validation, and their ratio was 64:20:16. However, we performed image forensic analysis on it, such as Error Level Analysis (ELA) and Noise Residual Analysis (NRA). This study used deep learning architectures that can automatically classify cerebellum diseases and compared these models, which included six D-CNNs models, six transfer learning models, and three ensemble models. Another important contribution of our study is the significant improvement in the classification efficiency by strategically integrating squeeze and excitation and label smoothing techniques. We show that fine-tuning significantly improves the diagnostic accuracy of both D-CNNs and transfer learning models on cerebellum MRI data. Notably, our combined models consistently achieve higher performance, with FusionNet-6 reaching an exceptional accuracy of 99.83 %. K-fold cross-validation was performed, yielding consistently high performance with per-class sensitivity and specificity above 99 %. The study also greatly enhances the impact of dataset augmentation techniques, including the use of segmented data to reveal complex interactions that can enhance the performance of some models or, in some cases, dramatically reduce the performance of specific models. These results underscore the immense potential of deep learning ensembles to provide highly accurate and robust diagnostic support for cerebellum diseases, paving the way for more objective and efficient clinical workflows.

准确和及时的小脑疾病分类是有效诊断的关键，但由于这些疾病固有的异质性和其神经影像学表现的微妙性，它仍然具有挑战性。本研究研究了一种新的多阶段集成框架，将SE块和分段辅助增强相结合，为有限的小脑疾病MRI数据量身定制。数据集包括来自四个类的3296个MRI扫描，我们将数据集分为三个部分：训练、测试和验证，它们的比例为64:20:16。然而，我们对其进行了图像取证分析，如误差水平分析（ELA）和噪声残留分析（NRA）。本研究使用了可以自动对小脑疾病进行分类的深度学习架构，并对这些模型进行了比较，其中包括6个d - cnn模型、6个迁移学习模型和3个集成模型。我们研究的另一个重要贡献是通过策略性地整合挤压和激励和标签平滑技术，显著提高了分类效率。我们发现微调显著提高了d - cnn和迁移学习模型在小脑MRI数据上的诊断准确性。值得注意的是，我们的组合模型始终如一地实现更高的性能，FusionNet-6达到了99.83%的卓越准确率。进行K-fold交叉验证，获得一致的高性能，每类灵敏度和特异性均在99%以上。该研究还极大地增强了数据集增强技术的影响，包括使用分段数据来揭示复杂的相互作用，这些相互作用可以增强某些模型的性能，或者在某些情况下显着降低特定模型的性能。这些结果强调了深度学习系统在为小脑疾病提供高度准确和强大的诊断支持方面的巨大潜力，为更客观和有效的临床工作流程铺平了道路。

{"title":"NeuroFusion: A forensic enriched ensemble framework for cerebellum disease classification","authors":"Abu Hanzala , Md Sajjad , Tanjila Akter , Harpreet Kaur , Md Sadekur Rahman","doi":"10.1016/j.neuri.2025.100251","DOIUrl":"10.1016/j.neuri.2025.100251","url":null,"abstract":"<div><div>Accurate and timely classification of cerebellar diseases is crucial for effective diagnostic, yet it remains challenging due to the inherent heterogeneity of these disorders and the subtlety of their neuroimaging manifestations. This study investigated a novel multi-stage ensemble framework integrating SE blocks and segmentation-assisted augmentation tailored for limited cerebellum disease MRI data. Dataset included 3296 MRI scans from four classes and we divided dataset into three parts: training, testing, and validation, and their ratio was 64:20:16. However, we performed image forensic analysis on it, such as Error Level Analysis (ELA) and Noise Residual Analysis (NRA). This study used deep learning architectures that can automatically classify cerebellum diseases and compared these models, which included six D-CNNs models, six transfer learning models, and three ensemble models. Another important contribution of our study is the significant improvement in the classification efficiency by strategically integrating squeeze and excitation and label smoothing techniques. We show that fine-tuning significantly improves the diagnostic accuracy of both D-CNNs and transfer learning models on cerebellum MRI data. Notably, our combined models consistently achieve higher performance, with FusionNet-6 reaching an exceptional accuracy of 99.83 %. K-fold cross-validation was performed, yielding consistently high performance with per-class sensitivity and specificity above 99 %. The study also greatly enhances the impact of dataset augmentation techniques, including the use of segmented data to reveal complex interactions that can enhance the performance of some models or, in some cases, dramatically reduce the performance of specific models. These results underscore the immense potential of deep learning ensembles to provide highly accurate and robust diagnostic support for cerebellum diseases, paving the way for more objective and efficient clinical workflows.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing brain tumor diagnostics and therapy (2023–2025): A global bibliometric perspective on innovation and collaboration 推进脑肿瘤诊断和治疗（2023-2025）：创新和合作的全球文献计量学视角

Neuroscience informatics

Pub Date : 2025-12-21 DOI: 10.1016/j.neuri.2025.100250

Rosnah Sutan , Afzal Hussain , Rizal Abdul Manaf , Zaleha Md Isa , Ashfaq Hussain

Background

Brain tumors present a daunting clinical challenge, necessitating unwavering innovation in diagnostics, imaging, and therapeutics. Emerging advances in artificial intelligence (AI), molecular biomarkers, and neuroimaging have transformed the research landscape.

Objective

The current research conducted bibliometric analysis to map global research trends in brain tumor diagnosis, imaging, and treatment strategies between 2023 and 2025, with a particular focus on AI applicability and biomarker-driven precision medicine.

Methods

A systematic literature search of the Scopus database was performed in July 2025 for English original research articles between 2023 and 2025. The search keywords included: “brain tumor,” “glioma,” “glioblastoma,” “meningioma,” “astrocytoma,” “diagnosis,” “MRI,” “CT,” “artificial intelligence,” “deep learning,” “machine learning,” “radiotherapy,” “chemotherapy,” “surgery,” “biomarkers,” “prognosis,” “segmentation,” and “classification.” Bibliographic data were analyzed using Biblioshiny to explore publication output, citation impact, prominent authors, institutional productivity, keyword trends, and collaboration networks.

Results

The analysis included 23,496 papers from over 93,000 researchers. It indicated a research boom in AI-enhanced diagnostics, radiomics, and individualized treatments. Both China and the U.S. were leading producers, but the U.S. recorded greater international collaboration and citation impact. Glioma classification, MRI-based segmentation, and deep learning applications were the most common topics. Collaboration networks were geographically focused, with a particular concentration in East Asia.

Conclusion

Brain tumor research is rapidly moving towards precision and AI-driven strategies. While there is a growing scientific output, more international and intersectoral collaboration is needed to make these advances translate to equitable clinical gain.

脑肿瘤是一项令人生畏的临床挑战，需要在诊断、成像和治疗方面进行坚定不移的创新。人工智能（AI）、分子生物标志物和神经影像学的新兴进展已经改变了研究领域。本研究通过文献计量学分析，绘制2023年至2025年全球脑肿瘤诊断、成像和治疗策略的研究趋势，特别关注人工智能的适用性和生物标志物驱动的精准医学。方法于2025年7月对Scopus数据库中2023 - 2025年间的英文原创研究论文进行系统的文献检索。搜索关键词包括：“脑肿瘤”、“胶质瘤”、“胶质母细胞瘤”、“脑膜瘤”、“星形细胞瘤”、“诊断”、“MRI”、“CT”、“人工智能”、“深度学习”、“机器学习”、“放疗”、“化疗”、“手术”、“生物标志物”、“预后”、“分割”和“分类”。使用Biblioshiny对书目数据进行分析，以探索出版物产出、引文影响、知名作者、机构生产力、关键词趋势和合作网络。该分析包括来自93,000多名研究人员的23,496篇论文。它预示着人工智能增强诊断、放射组学和个性化治疗方面的研究热潮。中国和美国都是主要生产国，但美国的国际合作和引用影响更大。胶质瘤分类、基于mri的分割和深度学习应用是最常见的主题。协作网络在地理上是集中的，尤其集中在东亚。结论脑肿瘤研究正迅速向精准化和人工智能驱动的方向发展。虽然科学产出不断增加，但需要更多的国际和部门间合作，以使这些进展转化为公平的临床收益。

{"title":"Advancing brain tumor diagnostics and therapy (2023–2025): A global bibliometric perspective on innovation and collaboration","authors":"Rosnah Sutan , Afzal Hussain , Rizal Abdul Manaf , Zaleha Md Isa , Ashfaq Hussain","doi":"10.1016/j.neuri.2025.100250","DOIUrl":"10.1016/j.neuri.2025.100250","url":null,"abstract":"<div><h3>Background</h3><div>Brain tumors present a daunting clinical challenge, necessitating unwavering innovation in diagnostics, imaging, and therapeutics. Emerging advances in artificial intelligence (AI), molecular biomarkers, and neuroimaging have transformed the research landscape.</div></div><div><h3>Objective</h3><div>The current research conducted bibliometric analysis to map global research trends in brain tumor diagnosis, imaging, and treatment strategies between 2023 and 2025, with a particular focus on AI applicability and biomarker-driven precision medicine.</div></div><div><h3>Methods</h3><div>A systematic literature search of the Scopus database was performed in July 2025 for English original research articles between 2023 and 2025. The search keywords included: “brain tumor,” “glioma,” “glioblastoma,” “meningioma,” “astrocytoma,” “diagnosis,” “MRI,” “CT,” “artificial intelligence,” “deep learning,” “machine learning,” “radiotherapy,” “chemotherapy,” “surgery,” “biomarkers,” “prognosis,” “segmentation,” and “classification.” Bibliographic data were analyzed using Biblioshiny to explore publication output, citation impact, prominent authors, institutional productivity, keyword trends, and collaboration networks.</div></div><div><h3>Results</h3><div>The analysis included 23,496 papers from over 93,000 researchers. It indicated a research boom in AI-enhanced diagnostics, radiomics, and individualized treatments. Both China and the U.S. were leading producers, but the U.S. recorded greater international collaboration and citation impact. Glioma classification, MRI-based segmentation, and deep learning applications were the most common topics. Collaboration networks were geographically focused, with a particular concentration in East Asia.</div></div><div><h3>Conclusion</h3><div>Brain tumor research is rapidly moving towards precision and AI-driven strategies. While there is a growing scientific output, more international and intersectoral collaboration is needed to make these advances translate to equitable clinical gain.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in deep learning for multimodal brain imaging: A comprehensive survey 深度学习多模态脑成像研究进展综述

Neuroscience informatics

Pub Date : 2025-12-19 DOI: 10.1016/j.neuri.2025.100252

Saif M. Balsabti , Rasool M. Al-Gburi , Raid gaib , Ali Mustafa , Shaimaa Khamees Ahmed , Ali Mahmoud Issa , Taha Mahmoud Al-Naimi , Rawan AlSaad , Ali M. Elhenidy

In recent years, the field of medical brain imaging has witnessed remarkable advancements with the integration of artificial intelligence (AI) and deep learning techniques. Traditional unimodal imaging methods, such as MRI and CT, often fall short in providing comprehensive insights into neurological disorders. To address these limitations, multimodal imaging, which combines various imaging modalities like MRI, CT, PET, and SPECT, has emerged as a powerful tool for enhanced diagnosis and treatment planning. This survey presents an in-depth review of the state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), used for brain tumor classification, segmentation, forecasting, and object detection. We also explore the potential of hybrid models that integrate machine learning and deep learning approaches. Furthermore, we highlight the significant developments in multimodal brain imaging techniques from 2019 to 2024 and discuss the future research directions needed to advance this field. By synthesizing the latest findings, this survey aims to provide a comprehensive understanding of the current landscape and future possibilities in multimodal medical brain imaging.

近年来，随着人工智能（AI）和深度学习技术的融合，医学脑成像领域取得了显著进展。传统的单峰成像方法，如MRI和CT，往往无法提供对神经系统疾病的全面了解。为了解决这些限制，多模态成像，结合了各种成像方式，如MRI、CT、PET和SPECT，已经成为增强诊断和治疗计划的有力工具。本调查深入回顾了最先进的深度学习模型，包括卷积神经网络（cnn）和视觉变压器（ViTs），用于脑肿瘤分类、分割、预测和目标检测。我们还探索了整合机器学习和深度学习方法的混合模型的潜力。此外，我们重点介绍了2019年至2024年多模态脑成像技术的重大发展，并讨论了未来需要推进该领域的研究方向。通过综合最新研究结果，本调查旨在提供对多模态医学脑成像的现状和未来可能性的全面了解。

{"title":"Advances in deep learning for multimodal brain imaging: A comprehensive survey","authors":"Saif M. Balsabti , Rasool M. Al-Gburi , Raid gaib , Ali Mustafa , Shaimaa Khamees Ahmed , Ali Mahmoud Issa , Taha Mahmoud Al-Naimi , Rawan AlSaad , Ali M. Elhenidy","doi":"10.1016/j.neuri.2025.100252","DOIUrl":"10.1016/j.neuri.2025.100252","url":null,"abstract":"<div><div>In recent years, the field of medical brain imaging has witnessed remarkable advancements with the integration of artificial intelligence (AI) and deep learning techniques. Traditional unimodal imaging methods, such as MRI and CT, often fall short in providing comprehensive insights into neurological disorders. To address these limitations, multimodal imaging, which combines various imaging modalities like MRI, CT, PET, and SPECT, has emerged as a powerful tool for enhanced diagnosis and treatment planning. This survey presents an in-depth review of the state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), used for brain tumor classification, segmentation, forecasting, and object detection. We also explore the potential of hybrid models that integrate machine learning and deep learning approaches. Furthermore, we highlight the significant developments in multimodal brain imaging techniques from 2019 to 2024 and discuss the future research directions needed to advance this field. By synthesizing the latest findings, this survey aims to provide a comprehensive understanding of the current landscape and future possibilities in multimodal medical brain imaging.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

15 Years of optimizers in medical deep learning: A systematic review 15年的医学深度学习优化器：系统回顾

Neuroscience informatics

Pub Date : 2025-12-19 DOI: 10.1016/j.neuri.2025.100249

Selorm Adablanu , Utpal Barman , Dulumani Das

Optimization algorithms are pivotal in training deep learning (DL) models for medical imaging, determining how efficiently models learn, generalize, and perform across modalities. This systematic review analyzed 69 peer-reviewed studies (2010–2025) on optimizer performance in classification, segmentation, and object detection tasks using MRI, CT, X-ray, ultrasound, histopathology, and ECG data, following PRISMA 2020 guidelines. Adaptive optimizers such as Adam and its variants were most common, offering rapid convergence in CNN-based classification, whereas SGD and momentum-based methods yielded stronger generalization in large-scale segmentation. Emerging techniques—Sharpness-Aware Minimization (SAM), Ranger, and AdamW—improved robustness under domain shift or noisy conditions. Hybrid and metaheuristic optimizers provided marginal accuracy gains but at higher computational cost. Common limitations included inconsistent hyperparameter reporting, limited external validation, and dataset bias toward North American cohorts. Optimizer effectiveness was found to be task- and architecture-dependent: adaptive methods suit small or noisy datasets, while momentum-based and hybrid methods enhance generalization for complex imaging. Future studies should emphasize standardized evaluation, transparent reporting, and diverse data to enable equitable and reproducible deployment of medical AI.

优化算法是训练医学成像深度学习（DL）模型的关键，它决定了模型学习、泛化和跨模式执行的效率。本系统综述分析了69项同行评议的研究（2010-2025），根据PRISMA 2020指南，使用MRI、CT、x射线、超声、组织病理学和ECG数据，分析了优化器在分类、分割和目标检测任务中的性能。Adam及其变体等自适应优化器是最常见的，在基于cnn的分类中提供快速收敛，而SGD和基于动量的方法在大规模分割中产生了更强的泛化。新兴技术——锐度感知最小化（SAM）、Ranger和adamw——提高了在域移位或噪声条件下的鲁棒性。混合和元启发式优化器提供了边际精度增益，但计算成本较高。常见的限制包括不一致的超参数报告，有限的外部验证，以及对北美队列的数据集偏差。优化器的有效性取决于任务和架构：自适应方法适用于小数据集或有噪声的数据集，而基于动量和混合方法增强了复杂成像的泛化。未来的研究应强调标准化评估、透明报告和多样化数据，以实现医疗人工智能的公平和可重复部署。

{"title":"15 Years of optimizers in medical deep learning: A systematic review","authors":"Selorm Adablanu , Utpal Barman , Dulumani Das","doi":"10.1016/j.neuri.2025.100249","DOIUrl":"10.1016/j.neuri.2025.100249","url":null,"abstract":"<div><div>Optimization algorithms are pivotal in training deep learning (DL) models for medical imaging, determining how efficiently models learn, generalize, and perform across modalities. This systematic review analyzed 69 peer-reviewed studies (2010–2025) on optimizer performance in classification, segmentation, and object detection tasks using MRI, CT, X-ray, ultrasound, histopathology, and ECG data, following PRISMA 2020 guidelines. Adaptive optimizers such as Adam and its variants were most common, offering rapid convergence in CNN-based classification, whereas SGD and momentum-based methods yielded stronger generalization in large-scale segmentation. Emerging techniques—Sharpness-Aware Minimization (SAM), Ranger, and AdamW—improved robustness under domain shift or noisy conditions. Hybrid and metaheuristic optimizers provided marginal accuracy gains but at higher computational cost. Common limitations included inconsistent hyperparameter reporting, limited external validation, and dataset bias toward North American cohorts. Optimizer effectiveness was found to be task- and architecture-dependent: adaptive methods suit small or noisy datasets, while momentum-based and hybrid methods enhance generalization for complex imaging. Future studies should emphasize standardized evaluation, transparent reporting, and diverse data to enable equitable and reproducible deployment of medical AI.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures 通过深度学习架构探索小波类型和窗口对基于肌电图的IONM的影响

Neuroscience informatics

Pub Date : 2025-12-18 DOI: 10.1016/j.neuri.2025.100253

Abdalla Nabil Elsharkawy , Nourhan Zayed

Intraoperative neuromonitoring (IONM) plays a critical role in preserving nerve function during high-risk surgeries through real-time monitoring of electromyographic (EMG) activity. Routine EMG analysis, in real-time, is complex and prone to variability. This work presents an end-to-end deep learning-based framework for accurate EMG signal classification of the nerve status using the discrete wavelet transform (DWT) mathematical technique. Four state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a CNN-LSTM ensemble, and a Transformer model, were tested with various Daubechies wavelet families (db1–db6) and window sizes (50–500 samples). The Transformer model performed superiorly in classification, achieving an outstanding accuracy of 98.13 %, an F1-score of 98.14 %, and a recall of 97.50 % using db1 and a 400-sample window. The results summed up that the use of wavelet-based time-frequency decomposition has a significant influence on enhancing classification performance, especially when utilized with deep learning models.

术中神经监测（IONM）通过实时监测肌电图（EMG）活动，在高危手术中起到保护神经功能的关键作用。常规的实时肌电图分析是复杂的，而且容易发生变化。这项工作提出了一个端到端的基于深度学习的框架，用于使用离散小波变换（DWT）数学技术对神经状态进行准确的肌电信号分类。四种最先进的深度学习架构，包括卷积神经网络（CNN）、长短期记忆（LSTM）、CNN-LSTM集成和Transformer模型，在不同的Daubechies小波家族（db1-db6）和窗口大小（50-500个样本）下进行了测试。Transformer模型在分类方面表现优异，使用db1和400个样本窗口时，准确率达到98.13%，f1得分为98.14%，召回率为97.50%。结果表明，使用基于小波的时频分解对提高分类性能有显著影响，特别是当与深度学习模型结合使用时。

{"title":"Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures","authors":"Abdalla Nabil Elsharkawy , Nourhan Zayed","doi":"10.1016/j.neuri.2025.100253","DOIUrl":"10.1016/j.neuri.2025.100253","url":null,"abstract":"<div><div>Intraoperative neuromonitoring (IONM) plays a critical role in preserving nerve function during high-risk surgeries through real-time monitoring of electromyographic (EMG) activity. Routine EMG analysis, in real-time, is complex and prone to variability. This work presents an end-to-end deep learning-based framework for accurate EMG signal classification of the nerve status using the discrete wavelet transform (DWT) mathematical technique. Four state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a CNN-LSTM ensemble, and a Transformer model, were tested with various Daubechies wavelet families (db1–db6) and window sizes (50–500 samples). The Transformer model performed superiorly in classification, achieving an outstanding accuracy of 98.13 %, an F1-score of 98.14 %, and a recall of 97.50 % using db1 and a 400-sample window. The results summed up that the use of wavelet-based time-frequency decomposition has a significant influence on enhancing classification performance, especially when utilized with deep learning models.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100253"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury 用ARIMA模型评价点采样对急性创伤性神经损伤脑生理信号单变量点区间预测的效果

Neuroscience informatics

Pub Date : 2025-12-17 DOI: 10.1016/j.neuri.2025.100248

Nuray Vakitbilir , Kevin Y. Stein , Tobias Bergmann , Noah Silvaggio , Amanjyot Singh Sainbhi , Abrar Islam , Logan Froese , Rakibul Hasan , Mansoor Hayat , Marcel Aries , Frederick A. Zeiler

High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO₂), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.

高分辨率的生理信号，如颅内压（ICP）和区域脑氧饱和度（rSO2），通过连续监测大脑自动调节和血管反应性，对治疗创伤性脑损伤（TBI）至关重要。这些信号提供了脑灌注动力学的基本见解，支持及时的临床干预。然而，这些数据的高时间分辨率在实时使用、集成到预测模型和计算效率方面带来了挑战。因此，分辨率降低技术对于简化数据，同时保留准确预测和建模所需的关键特征是必不可少的。利用人脑多组学分析和综合神经信息学（MAIN-HUB）实验室数据库、高频脑生理学数据集，我们旨在利用原始信号和衍生指数的自回归综合移动平均（ARIMA）模型，评估点采样分辨率降低对点和区间预测的影响。通过选择非重叠间隔内的第一个值来降低时间分辨率，范围从1分钟（min）到12小时窗口。在不同的时间分辨率下，共分析了125例患者的数据。结果表明，ARIMA模型在高分辨率（例如1分钟）下表现良好，以较低的误差捕获短期生理动力学。然而，随着分辨率的降低，误差和变异性增加，特别是对于平均动脉压（MAP）和脑灌注压（CPP）等信号，它们表现出快速或复杂的生理变化。研究结果强调了仔细平衡时间分辨率、模型性能和计算效率的必要性，特别是在临床环境中处理高频生理数据时。

{"title":"Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury","authors":"Nuray Vakitbilir , Kevin Y. Stein , Tobias Bergmann , Noah Silvaggio , Amanjyot Singh Sainbhi , Abrar Islam , Logan Froese , Rakibul Hasan , Mansoor Hayat , Marcel Aries , Frederick A. Zeiler","doi":"10.1016/j.neuri.2025.100248","DOIUrl":"10.1016/j.neuri.2025.100248","url":null,"abstract":"<div><div>High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO<sub>2</sub>), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG-based classification in psychiatry using motif discovery 基于脑电图的精神病学基序发现分类

Neuroscience informatics

Pub Date : 2025-11-12 DOI: 10.1016/j.neuri.2025.100242

Melanija Kraljevska , Kateřina Hlaváčková-Schindler , Lukas Miklautz , Claudia Plant

In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.

在目前的医疗实践中，由于抗抑郁药的明显效果延迟发作，接受抑郁症治疗的患者通常必须等待4到6周，临床医生才能评估他们对药物的反应。在早期阶段确定治疗反应是非常重要的，因为它可以减少与长期治疗相关的情绪和经济负担。我们提出了一种新的Motif发现框架（MDF），从脑电图时间序列数据中提取动态特征，以区分抑郁症治疗反应者和无反应者。我们的研究结果表明，MDF早在治疗第7天就可以高精度地预测治疗反应，显著减少患者的等待时间。此外，我们证明MDF可以很好地推广到其他精神疾病的分类任务，包括精神分裂症、阿尔茨海默病和痴呆症。总的来说，我们的实验表明MDF优于相关基准测试。我们的分类框架的高精度强调了EEG动态特性的潜力——以基序表示——以支持临床决策并最终提高患者的生活质量。

{"title":"EEG-based classification in psychiatry using motif discovery","authors":"Melanija Kraljevska , Kateřina Hlaváčková-Schindler , Lukas Miklautz , Claudia Plant","doi":"10.1016/j.neuri.2025.100242","DOIUrl":"10.1016/j.neuri.2025.100242","url":null,"abstract":"<div><div>In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification 基于注意力门控CNN和离散小波变换的脑出血分类集成框架

Neuroscience informatics

Pub Date : 2025-11-12 DOI: 10.1016/j.neuri.2025.100243

Srutanik Bhaduri , Rasel Mondal , Prateek Sarangi , Vinod Kumar Kurmi , Swati Goyal , Lovely Kaushal , Mahek Sodani , Tanmay Basu

Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.

脑出血或颅内出血（ICH）是一种需要快速诊断的危重医学病症。由于缺乏特定的空间特征识别，现有的卷积神经网络（CNN）模型往往难以区分类似的出血亚型，如硬膜外（EDH）和硬膜下（SDH）。本研究旨在开发一个强大的分类框架来应对这一挑战。我们提出了一个结合两个互补模型的集成框架。第一个是注意门控的二维CNN，用来突出细微的出血区域。第二种是多层离散小波变换（DWT）模型，该模型在频域分析图像，从3D脑体积中捕获更深层次的上下文和纹理信息。在RSNA、CQ500和一个新的GMC临床数据集上对所提出的集成模型进行了评估。实证研究表明，我们的模型在标准评估指标上始终优于最先进的方法，包括准确性、宏观平均AUC-ROC、特异性、敏感性和f1评分。注意力门控CNN和基于dwt的模型的新颖组合提供了更全面的特征表示，从而显著提高了ICH分类的准确性和鲁棒性，特别是在区分EDH和SDH等具有挑战性的亚型方面。

{"title":"Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification","authors":"Srutanik Bhaduri , Rasel Mondal , Prateek Sarangi , Vinod Kumar Kurmi , Swati Goyal , Lovely Kaushal , Mahek Sodani , Tanmay Basu","doi":"10.1016/j.neuri.2025.100243","DOIUrl":"10.1016/j.neuri.2025.100243","url":null,"abstract":"<div><div>Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0