Summary of Breast Cancer Diagnosis: a Comprehensive Exploration Of Explainable Artificial Intelligence (xai) Techniques, by Samita Bai et al.
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
by Samita Bai, Sidra Nasir, Rizwan Ahmed Khan, Sheeraz Arif, Alexandre Meyer, Hubert Konik
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 application of Explainable Artificial Intelligence (XAI) techniques in breast cancer detection and diagnosis is a crucial area of research. This paper reviews the integration of various XAI approaches with machine learning and deep learning models used in breast cancer detection and classification. The authors investigate the modalities of breast cancer datasets, including mammograms and ultrasounds, processed with AI, highlighting how XAI can lead to more accurate diagnoses and personalized treatment plans. XAI techniques such as SHAP, LIME, Grad-CAM, and others are explored, and their effectiveness in clinical settings is evaluated. The paper also discusses the challenges in implementing these techniques and the importance of developing standardized metrics for evaluating XAI’s effectiveness. By analyzing and discussing these findings, the authors aim to bridge the gap between complex AI models and practical healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Breast cancer is a major health issue that affects many women worldwide. This paper looks at how Artificial Intelligence (AI) can help diagnose breast cancer more accurately. They use something called Explainable AI (XAI) which helps doctors understand how the AI model made its decisions. The authors look at different ways to explain AI models, such as SHAP and LIME, and how they are used in breast cancer detection. The paper also talks about the importance of making sure these AI models work well in real-world medical settings. By understanding how these AI models make decisions, doctors can use them to make better treatment plans for patients. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning
