Summary of Multi-stain Multi-level Convolutional Network For Multi-tissue Breast Cancer Image Segmentation, by Akash Modi et al.
Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation
by Akash Modi, Sumit Kumar Jha, Purnendu Mishra, Rajiv Kumar, Kiran Aatre, Gursewak Singh, Shubham Mathur
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 A novel convolutional neural network (CNN)-based Multi-class Tissue Segmentation model for histopathology whole-slide Breast slides is proposed to classify tumors and segment other tissue regions such as Ducts, acini, DCIS, Squamous epithelium, Blood Vessels, Necrosis, etc. as a separate class. The model uses pixel-aligned non-linear merge across spatial resolutions to enable accurate detection of various classes and separate bad regions from tissue regions using multi-level context from different resolutions of WSI. The proposed model is stain and scanner invariant across data sources due to its training pipeline using 12 million patches generated with context-aware augmentations, which was evaluated on 23,000 patches for a completely new stain (Hematoxylin and Eosin) and scanner (Motic) from a different lab, achieving a mean IOU of 0.72. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to analyze digital images of breast tissue slides. It’s like taking a picture of a piece of tissue with a microscope! The goal is to identify certain features on the slide that can help doctors diagnose and treat cancer. Right now, there are problems with how we do this, because different labs might use different methods or equipment, which makes it hard to share information. This new method uses special computer algorithms to look at the pictures of breast tissue and identify what’s normal and what’s not. It even works if the lab uses a different type of stain or microscope! The results show that this method is pretty good at identifying different features on the slide, which can help doctors make better decisions about treatment. |
Keywords
» Artificial intelligence » Cnn » Neural network
