2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献_第4页

MED-TEX: Transfer and Explain Knowledge with Less Data from Pretrained Medical Imaging Models MED-TEX:用更少的数据从预训练的医学成像模型中转移和解释知识

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761709

Thanh Nguyen-Duc, He Zhao, Jianfei Cai, Dinh Q. Phung

引用次数: 1

A Neuropathological Hub Identification for Alzheimer’s Disease Via joint Analysis of Topological Structure and Neuropathological Burden 通过拓扑结构和神经病理负担的联合分析识别阿尔茨海默病的神经病理中枢

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761444

Defu Yang, Wenchao Li, Jingwen Zhang, Hui Shen, Minghan Chen, Wentao Zhu, Guorong Wu

{"title":"A Neuropathological Hub Identification for Alzheimer’s Disease Via joint Analysis of Topological Structure and Neuropathological Burden","authors":"Defu Yang, Wenchao Li, Jingwen Zhang, Hui Shen, Minghan Chen, Wentao Zhu, Guorong Wu","doi":"10.1109/ISBI52829.2022.9761444","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761444","url":null,"abstract":"Mounting evidence shows that the neuropathological burden associated with Alzheimer’s disease spreads along the network pathway and is often selectively accumulated at certain critical hub regions, resulting in a higher level of amyloid burden than their topological neighbors. However, current approaches for hub identification only focus on the topological structure of brain networks without considering the spatial distribution pattern of neuropathological burden residing within networks. In this work, we proposed a novel method for identifying neuropathological hubs that integrates both the neuropathological and topological information of brain networks based on multimodal neuroimages, where the removal of hubs will result in a maximum decomposition in brain networks as well as a minimum variation in neuropathological burdens. Experimental results on real datasets demonstrated that regions identified as neuropathological hubs suffer a greater risk of neuropathological damage than those of conventional approaches, supporting the consensus distribution between hub nodes and neuropathological burdens.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"26 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":"84157157","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

A Machine Learning Framework for Fully Automatic 3D Fetal Cardiac Ultrasound Evaluation 全自动3D胎儿心脏超声评估的机器学习框架

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761613

Manna E. Philip, Ana Ferrieira, Aishani Tomar, Sparsh Chawla, A. Welsh, G. Stevenson, A. Sowmya

引用次数: 0

A Multi-Scale Self-Attention Network to Discriminate Pulmonary Nodules 多尺度自关注网络鉴别肺结节

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761574

A. Moreno, A. Rueda, F. Martínez

引用次数: 0

Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results 头颈部Ct和Mr图像中危险器官的分割:基线结果

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761433

Gasper Podobnik, B. Ibragimov, P. Strojan, P. Peterlin, T. Vrtovec

引用次数: 1

Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function 有创冠状动脉造影的半监督冠状血管分割与连接保持功能

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761695

Haorui He, Abhirup Banerjee, M. Beetz, R. Choudhury, V. Grau

引用次数: 1

Locally Structured Low-Rank MR Image Reconstruction using Submatrix Constraints 基于子矩阵约束的局部结构低秩磁共振图像重建

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761692

X. Chen, Wenchuan Wu, M. Chiew

引用次数: 3

Leaders: Learnable Deep Radial Subsampling for Mri Reconstruction 领导:Mri重建的可学习深径向亚采样

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761544

Zhiwen Wang, Bowen Li, Wenjun Xia, Chenyu Shen, Mingzheng Hou, Hu Chen, Y. Liu, Jiliu Zhou, Yi Zhang

{"title":"Leaders: Learnable Deep Radial Subsampling for Mri Reconstruction","authors":"Zhiwen Wang, Bowen Li, Wenjun Xia, Chenyu Shen, Mingzheng Hou, Hu Chen, Y. Liu, Jiliu Zhou, Yi Zhang","doi":"10.1109/ISBI52829.2022.9761544","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761544","url":null,"abstract":"Recently, deep learning approaches have shown great promise in learning MRI subsampling. The majority of existing works have focused on optimizing Cartesian or equipment-constrained Gaussian-like subsampling, ignoring the question of learning radial subsampling. This paper proposes a simple learnable radial subsampling technique for compressed sensing MRI. The proposed approach exploits a radial subsampling for direct estimation of all radial spokes’ weights from radial sampling space. The proposed Learnable Deep Radial Subsampling (LEADERS) method can be easily integrated with any deep learning-based reconstruction algorithm. This method can provide reliable estimates in a deep learning manner. The effectiveness of the generated radial subsampling patterns is verified on two deep learning-based reconstruction models, with a large-scale, publicly available brain MRI datasets for two downsampling factors (R = 4 and 8). The numerical and visual experiments demonstrate that the learned radial subsampling patterns can be applied for different deep learning reconstruction models with different subsampling rates, and shows more efficient and effective results than the ones reconstructed using existing handcrafted radial subsampling patterns.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"5 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":"87866312","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

Cats: Complementary CNN and Transformer Encoders for Segmentation 猫:用于分割的互补CNN和变压器编码器

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761596

Hao Li, Dewei Hu, Han Liu, Jiacheng Wang, I. Oguz

{"title":"Cats: Complementary CNN and Transformer Encoders for Segmentation","authors":"Hao Li, Dewei Hu, Han Liu, Jiacheng Wang, I. Oguz","doi":"10.1109/ISBI52829.2022.9761596","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761596","url":null,"abstract":"Recently, deep learning methods have achieved state-of-the-art performance in many medical image segmentation tasks. Many of these are based on convolutional neural networks (CNNs). For such methods, the encoder is the key part for global and local information extraction from input images; the extracted features are then passed to the decoder for predicting the segmentations. In contrast, several recent works show a superior performance with the use of transformers, which can better model long-range spatial dependencies and capture low-level details. However, transformer as sole encoder underperforms for some tasks where it cannot efficiently replace the convolution based encoder. In this paper, we propose a model with double encoders for 3D biomedical image segmentation. Our model is a U-shaped CNN augmented with an independent transformer encoder. We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results. We evaluate our methods on three public datasets from three different challenges: BTCV, MoDA and Decathlon. Compared to the state-of-theart models with and without transformers on each task, our proposed method obtains higher Dice scores across the board.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"15 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":"89004030","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: 13

Improving the Detection of The Prostrate in Ultrasound Images Using Machine Learning Based Image Processing 基于机器学习的图像处理改进超声图像中前列腺的检测

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761639

Tao Peng, Yiyun Wu, Jing Cai

引用次数: 2