Summary of Fmdnn: a Fuzzy-guided Multi-granular Deep Neural Network For Histopathological Image Classification, by Weiping Ding et al.
FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
by Weiping Ding, Tianyi Zhou, Jiashuang Huang, Shu Jiang, Tao Hou, Chin-Teng Lin
First submitted to arxiv on: 22 Jul 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 the Fuzzy-guided Multi-granularity Deep Neural Network (FMDNN), a novel approach to histopathological image classification that mimics the multi-tiered diagnostic process of pathologists. The model extracts features at coarse, medium, and fine granularity, leveraging the information in histopathological images. Fuzzy logic is incorporated to address redundant key information and guide universal fuzzy features toward multi-granular features using a cross-attention module. The authors demonstrate improved accuracy and robustness on multiple public datasets compared to traditional classification methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for doctors to look at pictures of cells under a microscope to figure out if someone has a disease. Right now, computers can only look at one level of detail in these pictures, but doctors do it by looking at different levels of detail. The researchers came up with a new way for the computer to look at these pictures by taking into account all the different details. They used special math called fuzzy logic that helps the computer make sense of all this information. This new method is better than what computers are doing now and can help doctors make more accurate diagnoses. |
Keywords
» Artificial intelligence » Classification » Cross attention » Image classification » Neural network
