This is the official implementation of Cross-scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images.
Source code repository
https://github.com/hrlblab/cs-mil
Get our docker image
sudo docker pull ddrrnn123/cs-mil:2.0
docker run --rm -v [/Data2/CS-MIL_data]/input:/input/:ro -v [/Data2/CS-MIL_data]/output:/output --gpus all -it ddrrnn123/cs-mil:2.0Code language: JavaScript (javascript)Run Omni-Seg
(1) Run MIL_global_Stage1_Training.py to train the model.
(2) Run MIL_global_Stage1_Testing.py to test the model.
Description
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20X magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold:
(1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed;
(2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention;
(3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy.
Referece
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, and Yuankai Huo
ArXiv
Cross-scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images
Ruining Deng, Can Cui, Lucas W. Remedios, Shunxing Bao, R. Michael Womick, Sophie Chiron, Jia Li, Joseph T. Roland, Ken S. Lau, Qi Liu, Keith T. Wilson, Yaohong Wang, Lori A. Coburn, Bennett A. Landman, and Yuankai Huo
MMMI 2022