Summary of Pathm3: a Multimodal Multi-task Multiple Instance Learning Framework For Whole Slide Image Classification and Captioning, by Qifeng Zhou et al.
PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning
by Qifeng Zhou, Wenliang Zhong, Yuzhi Guo, Michael Xiao, Hehuan Ma, Junzhou Huang
First submitted to arxiv on: 13 Mar 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 PathM3, a novel framework for aligning whole slide images (WSIs) with diagnostic captions in computational histopathology. The current challenge lies in processing gigapixel WSIs and authentic diagnostic captions are scarce, making it difficult to train an effective model. To overcome these obstacles, the authors propose a multimodal, multi-task, multiple instance learning (MIL) framework that adapts a query-based transformer for WSI classification and captioning. The framework aggregates patch features using an MIL method that considers correlations among instances and leverages limited diagnostic captions through multi-task joint learning. Experimental results demonstrate the effectiveness of PathM3 in improving classification accuracy and caption generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors use computers to look at pictures of tissue samples to diagnose diseases more accurately. The problem is that these images are very large and hard to work with, and there aren’t many examples of what the correct diagnosis should be. To solve this, the authors created a new way to combine computer vision and natural language processing. This method uses patterns in the images to figure out which parts are important and then matches those parts to what the doctors wrote about the samples. The results show that this new method works better than other ways of doing things. |
Keywords
» Artificial intelligence » Classification » Multi task » Natural language processing » Transformer
