Summary of Advancing Histopathology-based Breast Cancer Diagnosis: Insights Into Multi-modality and Explainability, by Faseela Abdullakutty et al.
Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability
by Faseela Abdullakutty, Younes Akbari, Somaya Al-Maadeed, Ahmed Bouridane, Rifat Hamoudi
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents a review of multimodal techniques in breast cancer diagnosis, specifically focusing on the fusion of histopathology images with non-image data. The authors highlight the importance of integrating diverse data sources beyond conventional imaging to improve diagnostic accuracy and patient outcomes. They also emphasize the need for explainability in diagnostic processes using Explainable AI (XAI) to elucidate decision-making processes of complex algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Breast cancer detection is crucial to improving patient outcomes, and traditional diagnostic methods are being replaced by more advanced technologies. This paper looks at how combining different types of data, like images and non-image information, can lead to better diagnoses. It also talks about the importance of explaining how these new algorithms make their decisions, so doctors and patients understand why certain treatments are recommended. |
