Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
By Bin Li, Yin Li, Kevin W. Eliceiri. Computer Vision and Pattern Recognition, 2020
996 citations recorded by the source corpus.
We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/fc3a214aa4da4a4407c071f056d945bc41893875
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Faisal Mahmood
- Tiffany Y. Chen
- Richard J. Chen
- Rahul G. Krishnan
- Yalin Zheng
- Hao Bian
- Chengkuan Chen
- Yicong Li
In Computer Vision and Pattern Recognition
- Faisal Mahmood
- Richard J. Chen
- Rahul G. Krishnan
- Yalin Zheng
- Yanda Meng
- Hao Bian
- Zhucheng Shao
- Yifeng Wang
Citations in the corpus
- Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022, methodology): “many variations of MIL that have adapted image pretraining techniques such as VAE-GANs, SimCLR, and MOCO as instance-level feature extraction [45,62,84].”
- DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification (2022, methodology): “028 DS-MIL [18] 0.888 0 .”
- TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication (2021, methodology): “Traditional MIL methods as well as the latest MIL methods such as ABMIL, DSMIL and CLAM usually require a large number of epochs to converge.”