Computer methods and programs in biomedicine最新文献

{"title":"Dual-path neural network extracts tumor microenvironment information from whole slide images to predict molecular typing and prognosis of Glioma","authors":"Zehang Ning ,&nbsp;Bojie Yang ,&nbsp;Yuanyuan Wang ,&nbsp;Zhifeng Shi ,&nbsp;Jinhua Yu ,&nbsp;Guoqing Wu","doi":"10.1016/j.cmpb.2024.108580","DOIUrl":"10.1016/j.cmpb.2024.108580","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on multi-instance learning have potential in WSIs analysis, but the large number of patches from WSIs challenges the effective extraction of key local patch and neighboring patch microenvironment info. Therefore, this paper aims to develop an automatic neural network that effectively extracts tumor microenvironment information from WSIs to predict molecular typing and prognosis of glioma.</div></div><div><h3>Methods:</h3><div>In this paper, we proposed a dual-path pathology analysis (DPPA) framework to enhance the analysis ability of WSIs for glioma diagnosis. Firstly, to mitigate the impact of redundant patches and enhance the integration of salient patch information within a multi-instance learning context, we propose a two-stage attention-based dynamic multi-instance learning network. In the network, two-stage attention and dynamic random sampling are designed to integrate diverse image patch information in pivotal regions adaptively. Secondly, to unearth the wealth of spatial context inherent in WSIs, we build a spatial relationship information quantification module. This module captures the spatial distribution of patches that encompass a variety of tissue structures, shedding light on the tumor microenvironment.</div></div><div><h3>Results:</h3><div>A large number of experiments on three datasets, two in-house and one public, totaling 1,795 WSIs demonstrate the encouraging performance of the DPPA, with mean area under curves of 0.94, 0.85, and 0.88 in predicting Isocitrate Dehydrogenase 1, Telomerase Reverse Tranase, and 1p/19q respectively, and a mean C-index of 0.82 in prognosis prediction. The proposed model can also group tumors in existing tumor subgroups into good and bad prognoses, with P <span><math><mo>&lt;</mo></math></span> 0.05 on the Log-rank test.</div></div><div><h3>Conclusions:</h3><div>The results of multi-center experiments demonstrate that the proposed DPPA surpasses the state-of-the-art models across multiple metrics. Through ablation experiments and survival analysis, the outstanding analytical ability of this model is further validated. Meanwhile, based on the work related to the interpretability of the model, the reliability and validity of the model have also been strongly confirmed. All source codes are released at: <span><span>https://github.com/nzehang97/DPPA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108580"},"PeriodicalIF":4.9,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Transformers-based models and linked data for deep phenotyping in radiology","authors":"Lluís-F. Hurtado ,&nbsp;Luis Marco-Ruiz ,&nbsp;Encarna Segarra ,&nbsp;Maria Jose Castro-Bleda ,&nbsp;Aurelia Bustos-Moreno ,&nbsp;Maria de la Iglesia-Vayá ,&nbsp;Juan Francisco Vallalta-Rueda","doi":"10.1016/j.cmpb.2024.108567","DOIUrl":"10.1016/j.cmpb.2024.108567","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.</div></div><div><h3>Methods and Results:</h3><div>Transformers-based NLP Models, concretely pre-trained RoBERTa language models, were used to process radiology reports and annotate them identifying elements matching UMLS Concept Unique Identifiers (CUIs) definitions. CUIs were mapped into several biomedical ontologies useful for phenotyping (e.g., SNOMED-CT, HPO, ICD-10, FMA, LOINC, and ICPC2, among others) and represented as a lightweight ontology using OWL (Web Ontology Language) constructs. This process resulted in a Linked Knowledge Base (LKB), which allows running expressive queries to retrieve reports that comply with specific criteria using automatic reasoning.</div></div><div><h3>Conclusion:</h3><div>Although phenotyping tools mostly rely on relational databases, the combination of NLP and Linked Data technologies allows us to build scalable knowledge bases using standard ontologies from the Web of data. Our approach enables us to execute a pipeline which input is free text and automatically maps identified entities to a LKB that allows answering phenotyping queries. In this work, we have only used Spanish radiology reports, although it is extensible to other languages for which suitable corpora are available. This is particularly valuable in regional and national systems dealing with large research databases from different registries and cohorts and plays an essential role in the scalability of large data reuse infrastructures that require indexing and governing distributed data sources.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108567"},"PeriodicalIF":4.9,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BGCSL: An unsupervised framework reveals the underlying structure of large-scale whole-brain functional connectivity networks","authors":"Hua Zhang ,&nbsp;Weiming Zeng ,&nbsp;Ying Li ,&nbsp;Jin Deng ,&nbsp;Boyang Wei","doi":"10.1016/j.cmpb.2024.108573","DOIUrl":"10.1016/j.cmpb.2024.108573","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Inferring large-scale brain networks from functional magnetic resonance imaging (fMRI) provides more detailed and richer connectivity information, which is critical for gaining insight into brain structure and function and for predicting clinical phenotypes. However, as the number of network nodes increases, most existing methods suffer from the following limitations: (1) Traditional shallow models often struggle to estimate large-scale brain networks. (2) Existing deep graph structure learning models rely on downstream tasks and labels. (3) They rely on sparse postprocessing operations. To overcome these limitations, this paper proposes a novel framework for revealing large-scale functional brain connectivity networks through graph contrastive structure learning, called BGCSL.</div></div><div><h3>Methods:</h3><div>Unlike traditional supervised graph structure learning methods, this framework does not rely on labeled information. It consists of two important modules: sparse graph structure learner and graph contrastive learning (GCL). It employs dynamic augmentation in GCL to train a sparse graph structure learner, enabling it to capture the intrinsic structure of the data.</div></div><div><h3>Results:</h3><div>We conducted extensive experiments on 12 synthetic datasets and 2 public functional magnetic resonance imaging datasets, demonstrating the effectiveness of our proposed framework. In the synthetic datasets, particularly in cases where node features are insufficient, BGCSL still maintains state-of-the-art performance. More importantly, on the ABIDE-I and HCP-rest datasets, BGCSL improved the downstream task performance of GCN-based models, including the original GCN, dGCN, and ContrastPool, to varying degrees.</div></div><div><h3>Conclusion:</h3><div>Our proposed method holds significant potential as a valuable reference for future large-scale brain network estimation and representation and is conducive to supporting the exploration of more fine-grained biomarkers.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108573"},"PeriodicalIF":4.9,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully automated segmentation of brain and scalp blood vessels on multi-parametric magnetic resonance imaging using multi-view cascaded networks","authors":"Songxiong Wu ,&nbsp;Zilong Huang ,&nbsp;Mingyu Wang ,&nbsp;Ping Zeng ,&nbsp;Biwen Tan ,&nbsp;Panying Wang ,&nbsp;Bin Huang ,&nbsp;Naiwen Zhang ,&nbsp;Nashan Wu ,&nbsp;Ruodai Wu ,&nbsp;Yong Chen ,&nbsp;Guangyao Wu ,&nbsp;Fuyong Chen ,&nbsp;Jian Zhang ,&nbsp;Bingsheng Huang","doi":"10.1016/j.cmpb.2025.108584","DOIUrl":"10.1016/j.cmpb.2025.108584","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Neurosurgical navigation is a critical element of brain surgery, and accurate segmentation of brain and scalp blood vessels is crucial for surgical planning and treatment. However, conventional methods for segmenting blood vessels based on statistical or thresholding techniques have limitations. In recent years, deep learning-based methods have emerged as a promising solution for blood vessel segmentation, but the segmentation of small blood vessels and scalp blood vessels remains challenging. This study aimed to explore a solution to overcoming the challenges.</div></div><div><h3>Methods</h3><div>This study proposes a multi-view cascaded deep learning network (MVPC<img>Net) that combines multiple refinements, including multi-view learning, multi-parameter input, and a multi-view ensemble module. We evaluated the proposed method on a dataset of 155 patients, which included annotations for brain and scalp blood vessels. Five-fold cross-validation was conducted on the dataset to assess the performance of the network.</div></div><div><h3>Results</h3><div>Ablation experiments showed that the proposed refinements in MVPC<img>Net significantly improved the segmentation of small blood and low-contrast vessel performance, which segmented scalp blood vessels from the original images, increasing the Dice and the 95 % Hausdorf distance (HD), from 0.865 to 0.922 and from 1.28 mm to 0.47 mm, respectively, compared to the baseline model.</div></div><div><h3>Conclusions</h3><div>The proposed method in this study provided a fully automated and accurate segmentation of brain and scalp blood vessels, which is essential for neurosurgical navigation and has potential for clinical applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108584"},"PeriodicalIF":4.9,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration","authors":"Cristian Camilo Pulgarín-Ospina ,&nbsp;Rocío del Amor ,&nbsp;Julio José Silva-Rodríguez ,&nbsp;Adrián Colomer ,&nbsp;Valery Naranjo","doi":"10.1016/j.cmpb.2024.108577","DOIUrl":"10.1016/j.cmpb.2024.108577","url":null,"abstract":"<div><div>Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images, integrating AI-driven predictive insights. While the tool is capable of handling various image formats, its primary use case is for Whole Slide Imaging (WSI) in the TIFF format, specifically tailored for histopathology applications. This innovative integration not only revolutionizes accessibility but also democratizes the utilization of complex deep-learning models for pathologists unfamiliar with such tools. Moreover, to demonstrate the effectiveness of this approach, we present a use case centered on the diagnosis of spindle cell skin neoplasm involving multiple annotators. Additionally, we conduct a usability study, showing the feasibility of the developed tool.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108577"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques","authors":"Zafran Waheed ,&nbsp;Jinsong Gui ,&nbsp;Md Belal Bin Heyat ,&nbsp;Saba Parveen ,&nbsp;Mohd Ammar Bin Hayat ,&nbsp;Muhammad Shahid Iqbal ,&nbsp;Zouheir Aya ,&nbsp;Awais Khan Nawabi ,&nbsp;Mohamad Sawan","doi":"10.1016/j.cmpb.2024.108579","DOIUrl":"10.1016/j.cmpb.2024.108579","url":null,"abstract":"<div><h3>Background</h3><div>Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.</div></div><div><h3>Objective</h3><div>This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).</div></div><div><h3>Methods</h3><div>A dataset of 6000 endoscopic images of various GI diseases was utilized. Advanced image preprocessing techniques, including adaptive noise reduction and image detail enhancement, were employed to improve accuracy and interpretability. The model's performance was assessed in terms of accuracy, computational cost, and disk space usage.</div></div><div><h3>Results</h3><div>The proposed lightweight model achieved an exceptional overall accuracy of 99.38 %. It operates efficiently with a computational cost of 0.61 GFLOPs and occupies only 3.09 MB of disk space. Additionally, Grad-CAM visualizations demonstrated enhanced model saliency and interpretability, offering insights into the decision-making process of the model post-KD.</div></div><div><h3>Conclusion</h3><div>The proposed model represents a significant advancement in the diagnosis of GI diseases. It provides a cost-effective and efficient alternative to traditional deep neural network methods, overcoming their computational limitations and contributing valuable insights for improved clinical application.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108579"},"PeriodicalIF":4.9,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients","authors":"Riccardo Campi ,&nbsp;Antonio De Santis ,&nbsp;Paolo Colombo ,&nbsp;Paolo Scarpazza ,&nbsp;Marco Masseroli","doi":"10.1016/j.cmpb.2024.108574","DOIUrl":"10.1016/j.cmpb.2024.108574","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like profound hypoxemia, pulmonary opacities on radiography, or unexplained respiratory failure are present. It can be classified as mild, moderate or severe. H-CPAP therapy is recommended as the initial treatment approach for mild ARDS. Even though the efficacy of H-CPAP in managing patients with moderate-to-severe hypoxemia remains unclear, its use has increased for these cases in response to the emergence of the COVID-19 Pandemic. Using the electronic medical records (EMR) from the Pulmonology Department of Vimercate Hospital, in this study we develop and evaluate a Machine Learning (ML) system able to predict the failure of H-CPAP therapy on ARDS patients.</div></div><div><h3>Methods:</h3><div>The Vimercate Hospital EMR provides demographic information, blood tests, and vital parameters of all hospitalizations of patients who are treated with H-CPAP and diagnosed with ARDS. This data is used to create a dataset of 622 records and 38 features, with 70%–30% split between training and test sets. Different ML models such as <em>SVM</em>, <em>XGBoost</em>, <em>Neural Network</em>, <em>Random Forest</em>, and <em>Logistic Regression</em> are iteratively trained in a cross-validation fashion. We also apply a feature selection algorithm to improve predictions quality and reduce the number of features.</div></div><div><h3>Results and Conclusions:</h3><div>The <em>SVM</em> and <em>Neural Network</em> models proved to be the most effective, achieving final accuracies of 95.19% and 94.65%, respectively. In terms of F1-score, the models scored 88.61% and 87.18%, respectively. Additionally, the <em>SVM</em> and <em>XGBoost</em> models performed well with a reduced number of features (23 and 13, respectively). The <em>PaO2/FiO2 Ratio</em>, <em>C-Reactive Protein</em>, and <em>O2 Saturation</em> resulted as the most important features, followed by <em>Heartbeats</em>, <em>White Blood Cells</em>, and <em>D-Dimer</em>, in accordance with the clinical scientific literature.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108574"},"PeriodicalIF":4.9,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation","authors":"Yintong Liu ,&nbsp;U. Rajendra Acharya ,&nbsp;Jen Hong Tan","doi":"10.1016/j.cmpb.2024.108571","DOIUrl":"10.1016/j.cmpb.2024.108571","url":null,"abstract":"<div><h3>Background:</h3><div>Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.</div></div><div><h3>Methods:</h3><div>This systematic review examines deep learning techniques for synthetic data generation in healthcare, focusing on their ability to maintain data utility and enhance privacy. Studies from Scopus, Web of Science, PubMed, and IEEE databases published between 2019 and 2023 were analyzed. Key methods explored include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models. Evaluation metrics encompass data resemblance, utility, and privacy preservation, with special attention to privacy-enhancing methods like differential privacy and federated learning.</div></div><div><h3>Results:</h3><div>GANs and VAEs demonstrated robust capabilities in generating realistic synthetic data for tabular, signal, image, and multi-modal datasets. Privacy-preserving approaches such as differential privacy and adversarial training significantly reduced re-identification risks while maintaining data fidelity. However, challenges persist in preserving temporal correlations, reducing biases, and aligning with regulatory frameworks, particularly for longitudinal and high-dimensional data.</div></div><div><h3>Conclusion:</h3><div>Synthetic data generation holds significant potential for privacy-preserving data sharing in healthcare. Ongoing research is required to develop advanced algorithms and evaluation frameworks, ensuring synthetic data’s quality and privacy. Collaboration between technologists and policymakers is essential to create comprehensive guidelines, fostering secure and effective data sharing in healthcare.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108571"},"PeriodicalIF":4.9,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TD-STrans: Tri-domain sparse-view CT reconstruction based on sparse transformer","authors":"Yu Li ,&nbsp;Xueqin Sun ,&nbsp;Sukai Wang ,&nbsp;Lina Guo ,&nbsp;Yingwei Qin ,&nbsp;Jinxiao Pan ,&nbsp;Ping Chen","doi":"10.1016/j.cmpb.2024.108575","DOIUrl":"10.1016/j.cmpb.2024.108575","url":null,"abstract":"<div><h3>Background and objective</h3><div>Sparse-view computed tomography (CT) speeds up scanning and reduces radiation exposure in medical diagnosis. However, when the projection views are severely under-sampled, deep learning-based reconstruction methods often suffer from over-smoothing of the reconstructed images due to the lack of high-frequency information. To address this issue, we introduce frequency domain information into the popular projection-image domain reconstruction, proposing a Tri-Domain sparse-view CT reconstruction model based on Sparse Transformer (TD-STrans).</div></div><div><h3>Methods</h3><div>TD-STrans integrates three essential modules: the projection recovery module completes the sparse-view projection, the Fourier domain filling module mitigates artifacts and over-smoothing by filling in missing high-frequency details; the image refinement module further enhances and preserves image details. Additionally, a multi-domain joint loss function is designed to simultaneously enhance the reconstruction quality in the projection domain, image domain, and frequency domain, thereby further improving the preservation of image details.</div></div><div><h3>Results</h3><div>The results of simulation experiments on the lymph node dataset and real experiments on the walnut dataset consistently demonstrate the effectiveness of TD-STrans in artifact removal, suppression of over-smoothing, and preservation of structural fidelity.</div></div><div><h3>Conclusion</h3><div>The reconstruction results of TD-STrans indicate that sparse transformer across multiple domains can alleviate over-smoothing and detail loss caused by reduced views, offering a novel solution for ultra-sparse-view CT imaging.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108575"},"PeriodicalIF":4.9,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic analysis of fractal–fractional cancer model under chemotherapy drug with generalized Mittag-Leffler kernel","authors":"Hardik Joshi ,&nbsp;Mehmet Yavuz ,&nbsp;Osman Taylan ,&nbsp;Abdulaziz Alkabaa","doi":"10.1016/j.cmpb.2024.108565","DOIUrl":"10.1016/j.cmpb.2024.108565","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cancer’s complex and multifaceted nature makes it challenging to identify unique molecular and pathophysiological signatures, thereby hindering the development of effective therapies. This paper presents a novel fractal–fractional cancer model to study the complex interplay among stem cells, effectors cells, and tumor cells in the presence and absence of chemotherapy. The cancer model with effective treatment through chemotherapy drugs is considered and discussed in detail.</div></div><div><h3>Methods:</h3><div>The numerical method for the fractal–fractional cancer model with a generalized Mittag-Leffler kernel is presented. The Routh–Hurwitz stability criteria are applied to confirm the local asymptotically stability of an endemic equilibrium point of the cancer model without treatment and with effective treatment under some conditions. The existence and uniqueness criteria of the fractal–fractional cancer model are derived. Furthermore, the stability analysis of the fractal–fractional cancer model is performed.</div></div><div><h3>Results:</h3><div>The temporal concentration pattern of stem cells, effectors cells, tumor cells, and chemotherapy drugs are procured. The usage of chemotherapy drugs kills the tumor cells or decreases their density over time and as a consequence takes a longer time to reach to equilibrium point. The decay rate of stem cells and tumor cells plays a crucial role in cancer dynamics. The notable role of fractal dimensions along with fractional order is observed in capturing the cancer cell concentration.</div></div><div><h3>Conclusion:</h3><div>A dynamic analysis of the fractal–fractional cancer model is demonstrated to examine the impact of chemotherapy drugs with a generalized Mittag-Leffler kernel. The model possesses three equilibrium points among them two correspond to the cancer model without treatment namely the tumor-free equilibrium point and endemic equilibrium point. One additional endemic equilibrium point exists in the case of effective treatment through chemotherapy drugs. The Routh–Hurwitz stability criteria are applied to confirm the local asymptotically stability of an endemic equilibrium point of the cancer model with and without treatment under some conditions. The chemotherapy drug plays a crucial role in controlling the growth of tumor cells. The fractal–fractional operator provided robustness to study cancer dynamics by the inclusion of memory and heterogeneity.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"260 ","pages":"Article 108565"},"PeriodicalIF":4.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}