Summary of Multiple Instance Learning For Glioma Diagnosis Using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study, by Ekansh Chauhan et al.
Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study
by Ekansh Chauhan, Amit Sharma, Megha S Uppin, C.V. Jawahar, P.K. Vinod
First submitted to arxiv on: 24 Feb 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 The proposed study utilizes multiple instance learning to improve brain tumor diagnosis by developing novel performance benchmarks in glioma subtype classification across various datasets. By combining ResNet-50 with Double-Tier Feature Distillation, the approach achieves state-of-the-art AUCs for three-way glioma subtype classification on both IPD-Brain and TCGA-Brain datasets. The study also establishes new benchmarks for grading and detecting molecular biomarkers through H&E stained whole slide images. Moreover, it highlights a significant correlation between model decision-making processes and the diagnostic reasoning of pathologists. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve brain tumor diagnosis by developing more accurate ways to classify different types of glioma tumors. The study uses special computer algorithms to analyze brain scan images and identify specific features that can help doctors diagnose tumors more effectively. The approach is able to achieve very high accuracy rates, making it a valuable tool for diagnosing brain tumors. |
Keywords
» Artificial intelligence » Classification » Distillation » Resnet
