Multiple Instance Learning
Multiple Instance Learning (MIL) is a machine learning approach addressing the challenge of classifying data sets where labels are assigned to groups (bags) of instances, rather than individual instances. Current research heavily focuses on applying MIL to whole slide image (WSI) analysis in digital pathology, employing architectures like transformers and incorporating techniques such as attention mechanisms, pseudo-labeling, and graph neural networks to improve classification accuracy and interpretability. This work is significant because it allows for efficient analysis of large, complex medical images without requiring exhaustive pixel-level annotation, potentially leading to faster and more accurate diagnoses in healthcare.
Papers
M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis
Junyu Li, Ye Zhang, Wen Shu, Xiaobing Feng, Yingchun Wang, Pengju Yan, Xiaolin Li, Chulin Sha, Min He
Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning
Tianhang Nan, Yong Ding, Hao Quan, Deliang Li, Lisha Li, Guanghong Zhao, Xiaoyu Cui
An efficient framework based on large foundation model for cervical cytopathology whole slide image screening
Jialong Huang, Gaojie Li, Shichao Kan, Jianfeng Liu, Yixiong Liang
cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process
Yihang Chen, Tsai Hor Chan, Guosheng Yin, Yuming Jiang, Lequan Yu
HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment
K M Arefeen Sultan, Md Hasibul Husain Hisham, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella, Shireen Elhabian
Multiple Instance Verification
Xin Xu, Eibe Frank, Geoffrey Holmes