Pattern Recognition Letters最新文献_第8页

Cross-modality white matter lesion segmentation by modality de-indentification 基于模态去识别的跨模态白质病变分割

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-10 DOI: 10.1016/j.patrec.2025.11.020

Domen Preložnik, Žiga Špiclin

Multiple sclerosis (MS) diagnosis and prognosis relies heavily on the accurate detection and segmentation of white matter lesions (WML) in magnetic resonance imaging (MRI). Different MRI sequences, particularly Fluid-Attenuated Inversion Recovery (FLAIR) and Double Inversion Recovery (DIR), offer complementary information about lesions but are rarely simultaneously acquired in clinical imaging protocols. We introduce a novel self-supervised modality sequential unlearning (SSMSU) adaptation technique that employs modality de-identification to extract modality-invariant features from MRI images, improving WML segmentation regardless of the input modality. Building upon the public nnU-Net framework, we introduce auxiliary modality classifiers at each resolution level and utilize confusion loss to explicitly suppress the modality-specific features while training on alternating modality inputs. We evaluated the approach on in-house dataset of 28 MS patients with paired FLAIR and DIR, MSSEG 2016 dataset of 53 subjects with paired FLAIR and proton density (DP), and 22 FLAIR test cases of MSLesSeg 2024. All cases had expert-annotated WML segmentation as reference. Experiments involved within- and between-dataset validation, comparing performances of single- and multi-modality single-channel, and multi-modality multi-channel training strategies based on Dice Similarity Coefficient (DSC), Lesion-wise True Positive Rate (LTPR), and Lesion-wise False Discovery Rate (LFDR). On in-house and MSSEG 2016 the SSMSU achieved best DSC and LTPR among single-channel models, with LFDR levels comparable to best values, while it attained the same level of performance to multi-channel models that required paired FLAIR/DIR or FLAIR/DP modalities. It ranked 2nd among single-channel methods on MSLesSeg 2024. Effectively suppressing modality-related information resulted in a technique that is cross-modal and delivers a flexible and robust automated WML segmentation tool.

多发性硬化症（MS）的诊断和预后在很大程度上依赖于磁共振成像（MRI）对白质病变（WML）的准确检测和分割。不同的MRI序列，特别是液体衰减反转恢复（FLAIR）和双重反转恢复（DIR），提供了关于病变的补充信息，但在临床成像方案中很少同时获得。我们引入了一种新的自监督模态顺序学习（SSMSU）自适应技术，该技术利用模态去识别从MRI图像中提取模态不变的特征，从而提高了WML分割，而不管输入模态如何。在公共nnU-Net框架的基础上，我们在每个分辨率级别引入了辅助的情态分类器，并利用混淆损失在交替情态输入的训练中显式地抑制情态特定的特征。我们对28例具有配对FLAIR和DIR的MS患者的内部数据集，53例具有配对FLAIR和质子密度（DP）的MSSEG 2016数据集以及22例MSLesSeg 2024的FLAIR测试例进行了评估。所有案例均以专家标注的WML分割作为参考。实验涉及数据集内部和数据集之间的验证，比较基于骰子相似系数（DSC），病变真阳性率（LTPR）和病变假发现率（LFDR）的单、多模态单通道和多模态多通道训练策略的性能。在内部和MSSEG 2016中，SSMSU在单通道模型中实现了最佳DSC和LTPR，其LFDR水平可与最佳值相媲美，而它与需要配对FLAIR/DIR或FLAIR/DP模式的多通道模型达到了相同的性能水平。它在MSLesSeg 2024上的单通道方法中排名第二。有效地抑制与模态相关的信息产生了一种跨模态的技术，并提供了灵活而健壮的自动化WML分割工具。

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引用次数: 0

FsBAD: Data-efficient feature reconstruction for few-shot brain anomaly detection FsBAD：基于数据效率的脑异常检测特征重构

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.patrec.2025.11.016

Hussain Ahmad Madni , Hafsa Shujat , Axel De Nardin , Silvia Zottin , Gian Luca Foresti

Data efficiency remains a central challenge in brain anomaly detection, where annotated datasets are often scarce. Most existing methods are tailored to single-class settings and show limited ability to generalize. We introduce FsBAD, a feature reconstruction-based approach designed for few-shot brain anomaly detection with minimal supervision. FsBAD reconstructs a nominal version of an anomalous brain scan by leveraging a small set of aligned reference samples. To enhance reconstruction quality, we propose a novel feature alignment strategy that integrates regression with distribution regularization, promoting both semantic accuracy and nominal consistency. While FsBAD is optimized for brain imaging, we evaluate its generalization capabilities on liver and retina datasets. Experiments across all three domains show that FsBAD consistently outperforms state-of-the-art methods in both image-wise classification and pixel-wise anomaly localization, even in extremely low-shot (2- to 15-shot) settings. This demonstrates FsBAD’s potential as a scalable, data-efficient solution for brain anomaly detection and its robustness across medical imaging tasks.

数据效率仍然是脑异常检测的核心挑战，其中注释数据集往往是稀缺的。大多数现有方法都是针对单类设置定制的，并且泛化能力有限。我们介绍了FsBAD，一种基于特征重建的方法，用于在最小监督下进行少量脑异常检测。FsBAD通过利用一小组对齐的参考样本重建了一个异常大脑扫描的名义版本。为了提高重建质量，我们提出了一种新的特征对齐策略，该策略将回归与分布正则化相结合，提高了语义准确性和标称一致性。虽然FsBAD对脑成像进行了优化，但我们评估了它在肝脏和视网膜数据集上的泛化能力。在所有三个领域的实验表明，FsBAD在图像分类和像素异常定位方面始终优于最先进的方法，即使在极低的镜头（2到15个镜头）设置下也是如此。这证明了FsBAD作为一种可扩展的、数据高效的脑异常检测解决方案的潜力，以及它在医学成像任务中的鲁棒性。

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引用次数: 0

MixL-CNN: Lightweight multi-scale model for cross-domain aspect term extraction MixL-CNN：用于跨领域方面术语提取的轻量级多尺度模型

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.patrec.2025.11.004

Linhai Liu, Weijiang Li

Cross-domain aspect term extraction (CD-ATE) is vital for fine-grained analysis, but deploying large pre-trained models like BERT is often infeasible in resource-constrained scenarios due to high computational costs. To address this challenge, we propose MixL-CNN, a novel lightweight convolutional neural network designed for efficient and effective CD-ATE. MixL-CNN integrates two core innovations: (1) Mixed Multi-Scale Convolutional (MMSC) that capture diverse contextual dependencies at different granularities, and (2) a dynamic, attention-based feature adaptation mechanism that enhances domain-aware feature extraction by selectively emphasizing relevant feature channels. Extensive experiments conducted on standard Restaurant, Laptop, and Device benchmarks across six domain shifts demonstrate that MixL-CNN achieves a new state-of-the-art average F1-score of 54.25 (which is a +0.32 improvement over the prior SOTA model WoChMutiE). Ablation studies confirm the crucial complementary roles of the multi-scale architecture and the dynamic adaptation component. Critically, MixL-CNN exhibits exceptional efficiency, operating with only 0.87MB parameters (over 125x fewer than BERT model) and demonstrating significantly accelerated inference speeds 3x faster than specific SOTA non-BERT model WoChMutiE. This remarkable balance of performance and efficiency positions MixL-CNN as a robust and practical solution for deploying high-performance CD-ATE in real-world, low-resource settings.

跨领域方面术语提取（CD-ATE）对于细粒度分析至关重要，但是由于计算成本高，在资源受限的场景中部署像BERT这样的大型预训练模型通常是不可行的。为了解决这一挑战，我们提出了MixL-CNN，这是一种新型的轻量级卷积神经网络，专为高效的CD-ATE而设计。mix - cnn集成了两个核心创新：(1)混合多尺度卷积（MMSC）捕获不同粒度的上下文依赖关系；(2)动态的、基于注意力的特征自适应机制，通过选择性地强调相关特征通道来增强领域感知特征提取。在六个领域轮班的标准餐厅、笔记本电脑和设备基准测试中进行的广泛实验表明，mix - cnn达到了新的最先进的平均f1分数54.25（比先前的SOTA模型WoChMutiE提高了+0.32）。消融研究证实了多尺度结构和动态适应成分的重要互补作用。至关重要的是，mix - cnn表现出卓越的效率，仅使用0.87MB参数（比BERT模型少125倍以上），并显示出显著加速的推理速度，比特定的SOTA非BERT模型WoChMutiE快3倍。这种卓越的性能和效率平衡使mix - cnn成为在现实世界低资源环境中部署高性能CD-ATE的强大实用解决方案。

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引用次数: 0

TRIS: A multimodal and multitask framework for unifying text–image retrieval and referring image segmentation TRIS：一个统一文本图像检索和参考图像分割的多模态和多任务框架

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.patrec.2025.11.026

Zengzhi Qian , Yulong Sun , Weide Kang , Bingke Zhu , Jinqiao Wang

Existing text–image retrieval methods often underperform due to limited understanding of target objects in both text and images. To address this limitation, we propose TRIS, a multimodal and multitask framework that unifies text–image retrieval and referring image segmentation. TRIS accommodates four distinct text–image retrieval tasks and the referring image segmentation task. Through multitask coupled learning, features of the retrieval and segmentation interact, mutually facilitating multimodal feature learning, thereby enhancing the performance of both tasks. Moreover, by exploiting the masks predicted by the segmentation task, we suggest applying the reranking technique to further enhance the performance of the retrieval task. Simultaneously, capitalizing on the consistency of images in the retrieval task, we propose using consistent loss to improve the target consistency of the segmentation task. Experimentally, we validate the efficacy of the TRIS framework across multiple text–image retrieval and referring image segmentation datasets.

现有的文本图像检索方法由于对文本和图像中目标对象的理解有限，往往表现不佳。为了解决这一限制，我们提出了TRIS，一个多模式和多任务框架，统一了文本图像检索和参考图像分割。TRIS包含四种不同的文本图像检索任务和参考图像分割任务。通过多任务耦合学习，检索和分词的特征相互作用，相互促进多模态特征学习，从而提高两项任务的性能。此外，通过利用分割任务预测的掩码，我们建议应用重排序技术进一步提高检索任务的性能。同时，利用检索任务中图像的一致性，提出使用一致性损失来提高分割任务的目标一致性。实验验证了TRIS框架在多个文本图像检索和参考图像分割数据集上的有效性。

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引用次数: 0

Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models 基于条件扩散模型的脑mri异常检测的无监督对比分析

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-08 DOI: 10.1016/j.patrec.2025.11.014

Cristiano Patrício , Carlo Alberto Barbano , Attilio Fiandrotti , Riccardo Renzulli , Marco Grangetto , Luís F. Teixeira , João C. Neves

Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). In the context of brain MRIs, existing CA approaches rely on supervised contrastive learning or variational autoencoders (VAEs) using both healthy and unhealthy data, but such reliance on target samples is challenging in clinical settings. Unsupervised Anomaly Detection (UAD) learns a reference representation of healthy anatomy, eliminating the need for target samples. Deviations from this reference distribution can indicate potential anomalies. In this context, diffusion models have been increasingly adopted in UAD due to their superior performance in image generation compared to VAEs. Nonetheless, precisely reconstructing the anatomy of the brain remains a challenge. In this work, we bridge CA and UAD by reformulating contrastive analysis principles for the unsupervised setting. We propose an unsupervised framework to improve the reconstruction quality by training a self-supervised contrastive encoder on healthy images to extract meaningful anatomical features. These features are used to condition a diffusion model to reconstruct the healthy appearance of a given image, enabling interpretable anomaly localization via pixel-wise comparison. We validate our approach through a proof-of-concept on a facial image dataset and further demonstrate its effectiveness on four brain MRI datasets, outperforming baseline methods in anomaly localization on the NOVA benchmark.

对比分析（CA）通过对比目标组（例如，不健康受试者）与背景组（例如，健康受试者）特有的模式来检测异常。在脑mri的背景下，现有的CA方法依赖于使用健康和不健康数据的监督对比学习或变分自编码器（VAEs），但这种对目标样本的依赖在临床环境中是具有挑战性的。无监督异常检测（UAD）学习健康解剖结构的参考表示，消除了对目标样本的需要。偏离这个参考分布可以表明潜在的异常。在这种情况下，由于扩散模型在图像生成方面优于VAEs，因此在UAD中越来越多地采用扩散模型。尽管如此，精确地重建大脑的解剖结构仍然是一个挑战。在这项工作中，我们通过重新制定无监督设置的对比分析原则，架起了CA和UAD的桥梁。我们提出了一种无监督框架，通过在健康图像上训练自监督对比编码器来提取有意义的解剖特征，从而提高重建质量。这些特征用于调节扩散模型，以重建给定图像的健康外观，从而通过逐像素比较实现可解释的异常定位。我们通过面部图像数据集的概念验证验证了我们的方法，并进一步证明了它在四个脑MRI数据集上的有效性，在NOVA基准上的异常定位优于基线方法。

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引用次数: 0

Mitigating task randomness in graph few-shot learning 减少图少次学习中的任务随机性

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-20 DOI: 10.1016/j.patrec.2025.11.022

Shuzhen Rao , Jun Huang

In graph few-shot learning, meta-training tasks are sampled to improve the model’s ability to learn from limited nodes. Existing methods adapted from computer vision, generally employ random task sampling, which can lead to excessive task randomness. This hinders effective training on the graph as models struggle to adapt to tasks with substantial variations in classes and nodes. To address this issue, we propose a novel method called TRARM, i.e., Task RAndomness Reduced graph Meta-learning to mitigate adverse effects of excessive task randomness. Firstly, we design progressive grouping-based sampling to adjust combinations of classes and nodes by stages, thereby enabling more focused and efficient meta-training. Secondly, complementing sampling, a unified memory-based meta-update module is first deployed to effectively accumulate cross-task knowledge, improving both efficiency and stability of meta-learning. Despite its simplicity, comprehensive experiments demonstrate the superior performance of TRARM on four widely used benchmarks.

在图少射学习中，对元训练任务进行采样，以提高模型从有限节点学习的能力。现有的基于计算机视觉的方法通常采用随机任务抽样，这可能导致任务随机性过大。这阻碍了对图的有效训练，因为模型很难适应类和节点中存在大量变化的任务。为了解决这个问题，我们提出了一种名为TRARM的新方法，即任务随机性减少图元学习，以减轻过度任务随机性的不利影响。首先，我们设计了基于渐进式分组的采样，分阶段调整类和节点的组合，从而使元训练更加集中和高效。其次，在抽样的基础上，首先部署统一的基于记忆的元更新模块，有效地积累跨任务知识，提高元学习的效率和稳定性。尽管它很简单，但综合实验证明了TRARM在四个广泛使用的基准测试上的优越性能。

引用次数: 0

Hybrid CNN and SVM model for Alzheimer’s disease classification using categorical focal loss function 基于分类局灶损失函数的混合CNN和SVM模型用于阿尔茨海默病分类

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-24 DOI: 10.1016/j.patrec.2025.11.031

Wided Hechkel , Rim Missaoui , Abdelhamid Helali , Marco Leo

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. It attacks the elderly population, causing a dangerous cognitive decline and memory loss due to the degeneration and atrophy of brain neurons. Recent developments in machine learning techniques for the detection and classification of AD boost the early diagnosis and enable slowing the disease by adopting preclinical treatments. However, a major defect of these techniques is their high complexity architectures and their less generalizability, which provokes difficulties in clinical integration. This paper presents a new approach that combines convolutional neural network (CNN) and support vector machines (SVM) for the detection of AD. CNN stage enhances the accuracy of the system because it is an excellent feature extractor. SVM stage handles classification performance by optimizing the decision boundaries; meanwhile, it requires fewer hyperparameter updates compared to end-to-end CNN with Softmax classifier. SVM reduces the computational cost of the training. Experiments are conducted on the Kaggle dataset for Magnetic Resonance Imaging (MRI) brain images of AD. The hybrid model achieved accuracy scores of 98.52 %, 97.71 %, and 97.58 % for the training set, validation set, and testing set respectively, inference times per sample of 0.0588s, 0.0586s, and 0.0592s on the above three sets respectively. Obtained results confirm high effectiveness and potential prospect of the developed CNN-SVM model in early diagnosis of AD with reduced implementation complexity.

阿尔茨海默病（AD）是全球痴呆症的主要原因。它攻击老年人，由于大脑神经元的退化和萎缩，导致危险的认知能力下降和记忆丧失。机器学习技术用于阿尔茨海默病的检测和分类的最新发展促进了早期诊断，并通过采用临床前治疗来减缓疾病。然而，这些技术的一个主要缺陷是它们的结构高度复杂和不太普遍，这给临床整合带来了困难。本文提出了一种将卷积神经网络（CNN）与支持向量机（SVM）相结合的AD检测方法。CNN stage是一种优秀的特征提取器，提高了系统的准确率。支持向量机阶段通过优化决策边界来处理分类性能；同时，与使用Softmax分类器的端到端CNN相比，它需要更少的超参数更新。支持向量机减少了训练的计算量。在Kaggle数据集上对AD的磁共振成像（MRI）脑图像进行实验。混合模型在训练集、验证集和测试集上的准确率分别为98.52%、97.71%和97.58%，每样本推理次数分别为0.0588s、0.0586s和0.0592s。研究结果证实了所建立的CNN-SVM模型在AD早期诊断中的有效性和潜在的应用前景。

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引用次数: 0

Monocular 3D lane detection with geometry-guided transformation and contextual enhancement 基于几何引导变换和上下文增强的单目三维车道检测

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-26 DOI: 10.1016/j.patrec.2025.11.041

Chunying Song, Qiong Wang, Zeren Sun, Huafeng Liu

Monocular 3D lane detection is a critical yet challenging task in autonomous driving, largely due to the lack of depth cues, complex road geometries, and appearance variations in real-world environments. Existing approaches often depend on bird’s-eye-view transformations or rigid geometric assumptions, which may introduce projection artifacts and hinder generalization. In this paper, we present GeoCNet, a BEV-free framework that directly estimates 3D lanes in the perspective domain. The architecture incorporates three key components: a Geometry-Guided Spatial Transformer (GST) for adaptive multi-plane ground modeling, a Perception-Aware Feature Modulation (PFM) module for context-driven feature refinement, and a Structure-Aware Lane Decoder (SALD) that reconstructs lanes as curvature-regularized anchor-aligned sequences. Extensive experiments on the OpenLane dataset demonstrate that GeoCNet achieves competitive performance in overall accuracy and shows clear improvements in challenging conditions such as night scenes and complex intersections. Additional evaluation on the Apollo Synthetic dataset further confirms the robustness and cross-domain generalization of the proposed framework. These results underscore the effectiveness of jointly leveraging geometry and contextual cues for accurate and reliable monocular 3D lane detection. Our code has been released at https://github.com/chunyingsong/GeoCNet.

单目3D车道检测在自动驾驶中是一项关键但具有挑战性的任务，主要原因是缺乏深度线索、复杂的道路几何形状以及现实环境中的外观变化。现有的方法通常依赖于鸟瞰图变换或严格的几何假设，这可能会引入投影伪影并阻碍泛化。在本文中，我们提出了GeoCNet，这是一个无bev的框架，可以直接估计透视域中的3D车道。该架构包含三个关键组件：用于自适应多平面地面建模的几何引导空间变压器（GST），用于上下文驱动特征优化的感知感知特征调制（PFM）模块，以及用于将车道重建为曲率正则化锚对齐序列的结构感知车道解码器（SALD）。在OpenLane数据集上进行的大量实验表明，GeoCNet在整体精度方面取得了具有竞争力的性能，并在具有挑战性的条件下（如夜景和复杂的十字路口）显示出明显的改进。对Apollo合成数据集的额外评估进一步证实了所提出框架的鲁棒性和跨域泛化。这些结果强调了联合利用几何和上下文线索进行准确可靠的单目3D车道检测的有效性。我们的代码已在https://github.com/chunyingsong/GeoCNet上发布。

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引用次数: 0

Anatomical foundation models for brain MRIs 脑mri解剖基础模型

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.patrec.2025.11.028

Carlo Alberto Barbano , Matteo Brunello , Benoit Dufumier , Marco Grangetto , Alzheimer’s Disease Neuroimaging Initiative

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be a good indicator for different conditions, such as Alzheimer’s Disease. Using brain age for weakly supervised pre-training of DL models in transfer learning settings has also recently shown promising results, especially when dealing with data scarcity of different conditions. On the other hand, anatomical information of brain MRIs (e.g. cortical thickness) can provide important information for learning good representations that can be transferred to many downstream tasks. In this work, we propose AnatCL, an anatomical foundation model for structural brain MRIs that (i.) leverages anatomical information in a weakly contrastive learning approach, and (ii.) achieves state-of-the-art performances across many different downstream tasks. To validate our approach we consider 12 different downstream tasks for the diagnosis of different conditions such as Alzheimer’s Disease, autism spectrum disorder, and schizophrenia. Furthermore, we also target the prediction of 10 different clinical assessment scores using structural MRI data. Our findings show that incorporating anatomical information during pre-training leads to more robust and generalizable representations. Pre-trained models can be found at: https://github.com/EIDOSLAB/AnatCL.

神经成像中的深度学习（DL）在检测神经系统疾病和神经退行性疾病方面变得越来越重要。脑年龄是神经影像学的主要生物标志物之一，它已被证明是不同疾病的良好指标，如阿尔茨海默病。在迁移学习设置中使用脑年龄对DL模型进行弱监督预训练最近也显示出有希望的结果，特别是在处理不同条件下的数据稀缺性时。另一方面，脑mri的解剖信息（如皮质厚度）可以为学习良好的表征提供重要信息，这些表征可以转移到许多下游任务中。在这项工作中，我们提出了AnatCL，这是一种结构脑mri的解剖基础模型，它(i)在弱对比学习方法中利用解剖信息，（ii）在许多不同的下游任务中实现最先进的性能。为了验证我们的方法，我们考虑了12种不同的下游任务，用于诊断不同的疾病，如阿尔茨海默病、自闭症谱系障碍和精神分裂症。此外，我们还针对使用结构MRI数据预测10种不同的临床评估评分。我们的研究结果表明，在预训练中加入解剖信息会导致更稳健和可概括的表征。预训练模型可以在https://github.com/EIDOSLAB/AnatCL上找到。

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引用次数: 0

Multimodal Dynamic Cost Matrix Adaptation under data imbalance for multimodal sentiment analysis 数据不平衡下的多模态动态成本矩阵自适应

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-01 Epub Date: 2025-11-06 DOI: 10.1016/j.patrec.2025.11.005

Weiyang Wang , Haoyue Liu , Juan Huang , Bin Xu

Class imbalance in Multimodal Sentiment Analysis (MSA) introduces significant learning bias, causing models to favor majority classes and neglect minority emotions. Existing imbalance-handling methods, including oversampling, undersampling, and cost-sensitive learning, are primarily designed for unimodal tasks and cannot fully exploit cross-modal dependencies. Furthermore, current MSA approaches often employ fixed cost matrices and static loss weighting strategies, which fail to adapt to evolving data distributions, leading to suboptimal feature calibration and poor generalization under real-world conditions. To address these limitations, this paper proposes Multimodal Dynamic Cost Matrix Adaptation (MDCMA), a novel framework that dynamically adjusts class cost matrices to optimize loss allocation and improve minority-class representation. Specifically, MDCMA constructs learnable, modality-specific cost matrices for text and audio via a cross-modal cost matrix parameterization framework, regulated by an adaptive gating network to achieve precise feature space calibration. A sample-level dynamic loss weight balancing mechanism tracks global class statistics in real-time to emphasize minority classes, while a gradient-driven cost matrix optimization algorithm establishes a backpropagation-based feedback loop between cost parameters and classification loss. Experimental results on benchmark datasets demonstrate that MDCMA significantly improves performance under imbalanced conditions, offering a robust and generalizable solution to class imbalance in MSA.

多模态情绪分析（MSA）中的班级失衡引入了显著的学习偏差，导致模型偏向多数班级而忽视少数班级的情绪。现有的不平衡处理方法，包括过采样、欠采样和成本敏感学习，主要是为单模态任务设计的，不能充分利用跨模态依赖性。此外，目前的MSA方法通常采用固定成本矩阵和静态损失加权策略，这些策略无法适应不断变化的数据分布，导致现实条件下的特征校准不理想，泛化能力差。为了解决这些限制，本文提出了多模态动态成本矩阵自适应（MDCMA），这是一个动态调整类成本矩阵以优化损失分配和改善少数类表示的新框架。具体来说，MDCMA通过跨模态成本矩阵参数化框架为文本和音频构建可学习的、模态特定的成本矩阵，由自适应门控网络调节，以实现精确的特征空间校准。样本级动态损失权重平衡机制实时跟踪全局类统计，强调少数类，梯度驱动的代价矩阵优化算法在代价参数和分类损失之间建立了基于反向传播的反馈回路。在基准数据集上的实验结果表明，MDCMA显著提高了不平衡条件下的性能，为MSA中类不平衡问题提供了鲁棒性和可推广的解决方案。

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引用次数: 0