Summary of Mamba2mil: State Space Duality Based Multiple Instance Learning For Computational Pathology, by Yuqi Zhang et al.
Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
by Yuqi Zhang, Xiaoqian Zhang, Jiakai Wang, Yuancheng Yang, Taiying Peng, Chao Tong
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Mamba2MIL, a novel Multiple Instance Learning (MIL) framework for computational pathology. The proposed method addresses limitations in existing frameworks by introducing the state space duality model (SSD) to handle long sequences of patches from whole slide images (WSIs). Additionally, weighted feature selection and sequence transformation methods are developed to fuse diverse features and improve sequence information utilization. Experimental results demonstrate that Mamba2MIL outperforms state-of-the-art MIL methods on multiple datasets, including NSCLC and BRACS. Specifically, it achieves a binary tumor classification AUC of 0.9533 and an accuracy of 0.8794 on the NSCLC dataset, and a multiclass classification AUC of 0.7986 and an accuracy of 0.4981 on the BRACS dataset. The code is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to analyze medical images called Mamba2MIL. It helps doctors diagnose diseases better by looking at many small parts of a patient’s whole slide image. The method is special because it can handle different types of information and use it all together to make predictions. The researchers tested their method on two big datasets and found that it did much better than other methods. For example, it was able to correctly diagnose lung cancer with an accuracy of 87% and identify different types of breast cancer with an accuracy of 50%. You can find the code used in this research on a special website. |
Keywords
» Artificial intelligence » Auc » Classification » Feature selection
