Information processing in medical imaging : proceedings of the ... conference最新文献_第5页

Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows 协调流:基于规范化流的无监督MR协调

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-27 DOI: 10.48550/arXiv.2301.11551

Farzad Beizaee, Christian Desrosiers, G. Lodygensky, J. Dolz

引用次数: 2

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations 基于正交表征学习的多敏感属性医学图像分类公平性研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-04 DOI: 10.48550/arXiv.2301.01481

Wenlong Deng, Yuan Zhong, Qianming Dou, Xiaoxiao Li

{"title":"On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations","authors":"Wenlong Deng, Yuan Zhong, Qianming Dou, Xiaoxiao Li","doi":"10.48550/arXiv.2301.01481","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01481","url":null,"abstract":"Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"2014 1","pages":"158-169"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86517512","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: 5

MetaViT: Metabolism-Aware Vision Transformer for Differential Diagnosis of Parkinsonism with 18F-FDG PET MetaViT:代谢感知视觉转换与18F-FDG PET鉴别诊断帕金森病

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_11

Lin Zhao, Hexin Dong, P. Wu, Jiaying Lu, Le Lu, Jingren Zhou, Tianming Liu, Li Zhang, Ling Zhang, Yuxing Tang, C. Zuo

引用次数: 2

Multi-task Multi-instance Learning for Jointly Diagnosis and Prognosis of Early-Stage Breast Invasive Carcinoma from Whole-Slide Pathological Images 基于多任务多实例学习的早期乳腺浸润癌全片病理影像联合诊断与预后研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_12

Jianxin Liu, Rongjun Ge, Peng Wan, Qi Zhu, Daoqiang Zhang, Wei Shao

引用次数: 0

Source-Free Domain Adaptation for Medical Image Segmentation via Selectively Updated Mean Teacher 基于选择性更新均值教师的医学图像分割无源域自适应

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_18

Ziqi Wen, Xinru Zhang, Chuyang Ye

引用次数: 2

Super-Resolution Reconstruction of Fetal Brain MRI with Prior Anatomical Knowledge 胎儿脑MRI的超分辨率重建与先前的解剖学知识

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_33

Shijie Huang, Geng Chen, Kaicong Sun, Zhiming Cui, Xukun Zhang, P. Xue, Xuan Zhang, He-Xiao Zhang, Dinggang Shen

引用次数: 0

Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification 基于扩散模型的半监督学习脑出血图像中线偏移定量研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.48550/arXiv.2301.00409

Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, C. Mak, J. Abrigo, Q. Dou

{"title":"Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification","authors":"Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, C. Mak, J. Abrigo, Q. Dou","doi":"10.48550/arXiv.2301.00409","DOIUrl":"https://doi.org/10.48550/arXiv.2301.00409","url":null,"abstract":"Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"497 1","pages":"69-81"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81707350","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

Subtype and Stage Inference with Timescales 具有时间尺度的子类型和阶段推断

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_2

A. Young, Leon M. Aksman, Daniel C. Alexander, P. Wijeratne

引用次数: 2

Differentiable Gamma Index-Based Loss Functions: Accelerating Monte-Carlo Radiotherapy Dose Simulation 基于可微伽马指数的损失函数:加速蒙特卡罗放疗剂量模拟

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_37

S. Martinot, N. Komodakis, M. Vakalopoulou, N. Bus, C. Robert, É. Deutsch, N. Paragios

引用次数: 0

POLAFFINI: Efficient Feature-Based Polyaffine Initialization for Improved Non-linear Image Registration POLAFFINI:用于改进非线性图像配准的高效基于特征的多仿射初始化

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_47

Antoine Legouhy, Ross Callaghan, H. Azadbakht, Hui Zhang

引用次数: 0