2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献_第5页

Interpretable Graph Convolutional Network Of Multi-Modality Brain Imaging For Alzheimer’s Disease Diagnosis 多模态脑成像的可解释图卷积网络用于阿尔茨海默病诊断

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761449

Houliang Zhou, Lifang He, Yu Zhang, Li Shen, Brian Chen

{"title":"Interpretable Graph Convolutional Network Of Multi-Modality Brain Imaging For Alzheimer’s Disease Diagnosis","authors":"Houliang Zhou, Lifang He, Yu Zhang, Li Shen, Brian Chen","doi":"10.1109/ISBI52829.2022.9761449","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761449","url":null,"abstract":"Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer’s disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"31 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81205330","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: 11

Incremental Learning for a Flexible CAD System Design 基于增量学习的柔性CAD系统设计

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761688

Prathyusha Akundi, J. Sivaswamy

{"title":"Incremental Learning for a Flexible CAD System Design","authors":"Prathyusha Akundi, J. Sivaswamy","doi":"10.1109/ISBI52829.2022.9761688","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761688","url":null,"abstract":"Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained to learn new tasks sequentially, since the parameters of the model will change to optimize on the new class. The problem of alleviating CF is of interest to Computer aided diagnostic (CAD) systems community to facilitate class incremental learning (IL): learn new classes as and when new data/annotations are made available and old data is no longer accessible. However, IL has not been explored much in CAD development. We propose a novel approach that ensures that a model remembers the causal factor behind the decisions on the old classes, while incrementally learning new classes. We introduce a common auxiliary task during the course of incremental training, whose hidden representations are shared across all the classification heads. Since the hidden representation is no longer task-specific, it leads to a significant reduction in CF. We demonstrate our approach by incrementally learning 5 different tasks on Chest-Xrays and compare the results with the state-of-the-art regularization methods. Our approach performs consistently well in reducing CF in all the tasks with almost zero CF in most of the cases unlike standard regularisation-based approaches.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81507393","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: 1

Weakly Supervised Multimodal 30-Day All-Cause Mortality Prediction for Pulmonary Embolism Patients 弱监督下肺栓塞患者30天全因死亡率预测

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761700

Noa Cahan, E. Marom, S. Soffer, Y. Barash, E. Konen, E. Klang, H. Greenspan

引用次数: 2

Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision 带监督的序列图变分自编码器对AD纵向Β-Amyloid的解纠缠表示

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761588

Fan Yang, Guorong Wu, Won Hwa Kim

{"title":"Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision","authors":"Fan Yang, Guorong Wu, Won Hwa Kim","doi":"10.1109/ISBI52829.2022.9761588","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761588","url":null,"abstract":"The emergence of Positron Emission Tomography (PET) imaging allows us to quantify the burden of amyloid plaques in-vivo, which is one of the hallmarks of Alzheimer’s disease (AD). However, the invasive exposure to radiation and high imaging cost significantly restrict the application of PET in characterizing the evolution of pathology burden which often requires longitudinal PET image sequences. In this regard, we propose a proof-of-concept solution to generate the complete trajectory of pathological events throughout the brain based on very limited number of PET scans. We present a novel variational autoencoder model to learn a latent population-level representation of neurodegeneration process based on the longitudinal β-amyloid measurements at each brain region and longitudinal diagnostic stages. As the propagation of pathological burdens follow the topology of brain connectome, we further cast our neural network into a supervised sequential graph VAE, where we use the brain network to guide the representation learning. Experiments show that the disentangled representation can capture disease-related dynamics of amyloid and forecast the level of amyloid depositions at future time points.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91292639","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

Aortic Arch Anatomy Characterization from MRA: A CNN-Based Segmentation Approach 基于MRA的主动脉弓解剖特征:一种基于cnn的分割方法

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761708

Mounir Lahlouh, Y. Chenoune, R. Blanc, J. Szewczyk, Nicolas Passat

{"title":"Aortic Arch Anatomy Characterization from MRA: A CNN-Based Segmentation Approach","authors":"Mounir Lahlouh, Y. Chenoune, R. Blanc, J. Szewczyk, Nicolas Passat","doi":"10.1109/ISBI52829.2022.9761708","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761708","url":null,"abstract":"Neurovascular pathologies are often treated with the help of imaging to guide catheters inside arteries. However, positioning a microcatheter into the aortic arch and threading it through blood vessels for embolization, mechanical thrombectomy or stenting is a challenging task. Indeed, adverse aortic arch anatomies are frequently encountered, especially when the aortic arch is dilated, or the supra-aortic branches are elongated and tortuous. In this article, we propose a pipeline using convolutional neural networks for the segmentation of the aortic arch from magnetic resonance images for further anatomy classification purpose. This pipeline is composed of two successive modules, dedicated to the localization and the accurate segmentation of the aortic arch and the origin of supra-aortic branches, respectively. These segmentations are then used to generate 3D models from which the anatomy and the type of the aortic arches can be characterized. A quantitative evaluation of this approach, carried out on various U-Net architectures and different optimizers, leads to satisfactory segmentation results, then allowing a reliable characterization.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"2 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91334649","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: 2

Two-Stage Topological Refinement Network for Retinal Artery/Vein Classification 视网膜动/静脉分类的两阶段拓扑细化网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761669

Shichen Luo, Zhan Heng, M. Pagnucco, Yang Song

引用次数: 4

Counterfactual Explainable Gastrointestinal and Colonoscopy Image Segmentation 反事实的可解释胃肠道和结肠镜图像分割

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761664

Divij G. Singh, Ayush Somani, A. Horsch, Dilip K. Prasad

引用次数: 2

Quantifying Newly Appearing Replication FOCI in Cell Nuclei Based on 3d Non-Rigid Registration 基于三维非刚性配准的细胞核新出现的复制FOCI定量

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761689

Qi Gao, Vadim O. Chagin, M. C. Cardoso, K. Rohr

引用次数: 0

Joint Attention for Medical Image Segmentation 医学图像分割的联合关注

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761624

Mo Zhang, Bin Dong, Quanzheng Li

引用次数: 0

Active Index: An Integrated Index to Reveal Disrupted Brain Network Organizations of Major Depressive Disorder Patients 活跃指数:揭示重度抑郁症患者脑网络组织紊乱的综合指数

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761503

Yu Fu, Yanyan Huang, Meng Niu, Le Xue, Shunjie Dong, Shunlin Guo, J. Lei, Cheng Zhuo

{"title":"Active Index: An Integrated Index to Reveal Disrupted Brain Network Organizations of Major Depressive Disorder Patients","authors":"Yu Fu, Yanyan Huang, Meng Niu, Le Xue, Shunjie Dong, Shunlin Guo, J. Lei, Cheng Zhuo","doi":"10.1109/ISBI52829.2022.9761503","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761503","url":null,"abstract":"Altered functional brain networks have been a typical manifestation that distinguishes major depressive disorder (MDD) patients from healthy control (HC) subjects in functional magnetic resonance imaging (fMRI) studies. Recently, rich club and diverse club metrics have been proposed for network or network neuroscience analyses. The rich club defines a set of nodes that tend to be the hubs of specific communities, and the diverse club defines the nodes that span more communities and have edges diversely distributed across different communities. Considering the heterogeneity of rich clubs and diverse clubs, combining them and on the basis to derive a novel indicator may reveal new evidence of brain functional integration and separation, which might provide new insights into MDD. This study for the first time discussed the differences between MDD and HC using both rich club and diverse club metrics and found the complementarity of them in analyzing brain networks. Besides, a novel index, termed \"active index\", has been proposed in this study. The active index defines a group of nodes that tend to be diversely distributed across communities while avoiding being a hub of a community. Experimental results demonstrate the superiority of active index in analyzing MDD brain mechanisms.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"133 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86330357","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: 2