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Machine Learning in Medical Imaging

15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings, Part I

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Xuanang Xu

    1. Rensselaer Polytechnic Institute, Troy, USA

  2. Zhiming Cui

    1. ShanghaiTech University, Shanghai, China

  3. Islem Rekik

    1. Imperial College London, London, UK

  4. Xi Ouyang

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

  5. Kaicong Sun

    1. ShanghaiTech University, Shanghai, China

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15241)

Included in the following conference series:

Conference proceedings info: MLMI 2024.

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About this book

This book constitutes the proceedings of the 15th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 6, 2024.

The 63 full papers presented in this volume were carefully reviewed and selected from 100 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 using artificial intelligence (AI) and machine learning (ML).

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Table of contents (40 papers)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation

    • Koushik Biswas, Ridam Pal, Shaswat Patel, Debesh Jha, Meghana Karri, Amit Reza et al.
    Pages 1-11

  3. Generalizable Lymph Node Metastasis Prediction in Pancreatic Cancer

    • Jiaqi Qu, Xunbin Wei, Xiaohua Qian
    Pages 12-21

  4. IRUM: An Image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound Images

    • Haoyuan Chen, Yonghao Li, Jiadong Zhang, Qi Xu, Meiyu Li, Zhenhui Li et al.
    Pages 22-30

  5. Classification, Regression and Segmentation Directly from K-Space in Cardiac MRI

    • Ruochen Li, Jiazhen Pan, Youxiang Zhu, Juncheng Ni, Daniel Rueckert
    Pages 31-41

  6. DDSB: An Unsupervised and Training-Free Method for Phase Detection in Echocardiography

    • Zhenyu Bu, Yang Liu, Jiayu Huo, Jingjing Peng, Kaini Wang, Guangquan Zhou et al.
    Pages 42-51

  7. Mitral Regurgitation Recogniton Based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification

    • Zhe Liu, Xiliang Zhu, Tong Han, Yuhao Huang, Jian Wang, Lian Liu et al.
    Pages 52-62

  8. Deep Reinforcement Learning with Multiple Centerline-Guidance for Localization of Left Atrial Appendage Orifice from CT Images

    • Jongum Yoon, Sunghee Jung, Byunghwan Jeon
    Pages 63-72

  9. Lung-CADex: Fully Automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images

    • Furqan Shaukat, Syed Muhammad Anwar, Abhijeet Parida, Van Khanh Lam, Marius George Linguraru, Mubarak Shah
    Pages 73-82

  10. CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression

    • Caiwen Jiang, Xiaodan Xing, Zaixin Ou, Mianxin Liu, Walsh Simon, Guang Yang et al.
    Pages 83-93

  11. Vision Transformer Model for Automated End-to-End Radiographic Assessment of Joint Damage in Psoriatic Arthritis

    • Darshana Govind, Zijun Gao, Chaitanya Parmar, Kenneth Broos, Nicholas Fountoulakis, Lenore Noonan et al.
    Pages 94-103

  12. CorticalEvolve: Age-Conditioned Ordinary Differential Equation Model for Cortical Surface Reconstruction

    • Wenxuan Wu, Tong Xiong, Dongzi Shi, Ruowen Qu, Xiangmin Xu, Xiaofen Xing et al.
    Pages 104-113

  13. CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-Assisted Implicit Neural Representation Learning

    • Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Jing Yang, Wenxin Fan et al.
    Pages 114-123

  14. Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images

    • Jan Kybic, David Pakizer, Jiří Kozel, Patricie Michalčová, František Charvát, David Školoudík
    Pages 124-132

  15. Leveraging IHC Staining to Prompt HER2 Status Prediction from HE-Stained Histopathology Whole Slide Images

    • Yuping Wang, Dongdong Sun, Jun Shi, Wei Wang, Zhiguo Jiang, Haibo Wu et al.
    Pages 133-142

  16. VIMs: Virtual Immunohistochemistry Multiplex Staining via Text-to-Stain Diffusion Trained on Uniplex Stains

    • Shikha Dubey, Yosep Chong, Beatrice Knudsen, Shireen Y. Elhabian
    Pages 143-155

  17. Structural-Connectivity-Guided Functional Connectivity Representation for Multi-modal Brain Disease Classification

    • Zhaoxiang Wu, Biao Jie, Wen Li, Wentao Jiang, Yang Yang, Tongchun Du
    Pages 156-165

  18. Clinical Brain MRI Super-Resolution with 2D Slice-Wise Diffusion Model

    • Runqi Wang, Zehong Cao, Yichu He, Jiameng Liu, Feng Shi, Dinggang Shen
    Pages 166-176

  19. Low-to-High Frequency Progressive K-Space Learning for MRI Reconstruction

    • Xiaohan Xing, Liang Qiu, Lequan Yu, Lingting Zhu, Lei Xing, Lianli Liu
    Pages 177-186

  20. LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging

    • Hemant Kumar Aggarwal, Sudhanya Chatterjee, Dattesh Shanbhag, Uday Patil, K.V.S. Hari
    Pages 187-196
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Other volumes

  1. Machine Learning in Medical Imaging

  2. Machine Learning in Medical Imaging

Editors and Affiliations

  • Rensselaer Polytechnic Institute, Troy, USA

    Xuanang Xu

  • ShanghaiTech University, Shanghai, China

    Zhiming Cui, Kaicong Sun

  • Imperial College London, London, UK

    Islem Rekik

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

    Xi Ouyang

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