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Machine Learning in Medical Imaging

13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

  • Conference proceedings
  • © 2022

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Editors:
  1. Chunfeng Lian

    1. Xi'an Jiaotong University, Xi'an, China

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  2. Xiaohuan Cao

    1. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

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  3. Islem Rekik

    1. Istanbul Technical University, Istanbul, Turkey

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  4. Xuanang Xu

    1. Rensselaer Polytechnic Institute, Troy, USA

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  5. Zhiming Cui

    1. ShanghaiTech University, Pudong, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13583)

Included in the following conference series:

Conference proceedings info: MLMI 2022.

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Table of contents (48 papers)

  1. Front Matter

    Pages i-xiii
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  2. Function MRI Representation Learning via Self-supervised Transformer for Automated Brain Disorder Analysis

    • Qianqian Wang, Lishan Qiao, Mingxia Liu
    Pages 1-10

  3. Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning

    • Junghwan Lee, Tingyi Wanyan, Qingyu Chen, Tiarnan D. L. Keenan, Benjamin S. Glicksberg, Emily Y. Chew et al.
    Pages 11-20

  4. Region-Guided Channel-Wise Attention Network for Accelerated MRI Reconstruction

    • Jingshuai Liu, Chen Qin, Mehrdad Yaghoobi
    Pages 21-31

  5. Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-Teacher Multi-target Domain Adaptation

    • Linkai Peng, Li Lin, Pujin Cheng, Huaqing He, Xiaoying Tang
    Pages 32-42

  6. Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

    • Yongsong Huang, Qingzhong Wang, Shinichiro Omachi
    Pages 43-52

  7. 3D Segmentation with Fully Trainable Gabor Kernels and Pearson’s Correlation Coefficient

    • Ken C. L. Wong, Mehdi Moradi
    Pages 53-61

  8. A More Design-Flexible Medical Transformer for Volumetric Image Segmentation

    • Xin You, Yun Gu, Junjun He, Hui Sun, Jie Yang
    Pages 62-71

  9. Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System

    • Han Zhang, Tony C. W. Mok, Albert C. S. Chung
    Pages 72-80

  10. Plug-and-Play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping

    • Yunxiang Li, Ruilong Dan, Shuai Wang, Yifan Cao, Xiangde Luo, Chenghao Tan et al.
    Pages 81-90

  11. A Coarse-to-Fine Network for Craniopharyngioma Segmentation

    • Yijie Yu, Lei Zhang, Xin Shu, Zizhou Wang, Chaoyue Chen, Jianguo Xu
    Pages 91-100

  12. Patch-Level Instance-Group Discrimination with Pretext-Invariant Learning for Colitis Scoring

    • Ziang Xu, Sharib Ali, Soumya Gupta, Simon Leedham, James E. East, Jens Rittscher
    Pages 101-110

  13. AutoMO-Mixer: An Automated Multi-objective Mixer Model for Balanced, Safe and Robust Prediction in Medicine

    • Xi Chen, Jiahuan Lv, Dehua Feng, Xuanqin Mou, Ling Bai, Shu Zhang et al.
    Pages 111-120

  14. Memory Transformers for Full Context and High-Resolution 3D Medical Segmentation

    • Loic Themyr, Clément Rambour, Nicolas Thome, Toby Collins, Alexandre Hostettler
    Pages 121-130

  15. Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification

    • Qingxia Wu, Hongna Tan, Yaping Wu, Pei Dong, Jifei Che, Zheren Li et al.
    Pages 131-139

  16. Cross Task Temporal Consistency for Semi-supervised Medical Image Segmentation

    • Govind Jeevan, S. J. Pawan, Jeny Rajan
    Pages 140-150

  17. U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?

    • Xi Jia, Joseph Bartlett, Tianyang Zhang, Wenqi Lu, Zhaowen Qiu, Jinming Duan
    Pages 151-160

  18. UNet-eVAE: Iterative Refinement Using VAE Embodied Learning for Endoscopic Image Segmentation

    • Soumya Gupta, Sharib Ali, Ziang Xu, Binod Bhattarai, Ben Turney, Jens Rittscher
    Pages 161-170

  19. Dynamic Linear Transformer for 3D Biomedical Image Segmentation

    • Zheyuan Zhang, Ulas Bagci
    Pages 171-180

  20. Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-Stream Fully Convolutional Neural Network

    • Mohammadreza Negahdar
    Pages 181-190
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Other volumes

  1. Machine Learning in Medical Imaging

Keywords

About this book

This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022.
The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

Editors and Affiliations

  • Xi'an Jiaotong University, Xi'an, China

    Chunfeng Lian

  • Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Xiaohuan Cao

  • Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

  • Rensselaer Polytechnic Institute, Troy, USA

    Xuanang Xu

  • ShanghaiTech University, Pudong, China

    Zhiming Cui

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