Summary of Rad4xcnn: a New Agnostic Method For Post-hoc Global Explanation Of Cnn-derived Features by Means Of Radiomics, By Francesco Prinzi et al.
Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics
by Francesco Prinzi, Carmelo Militello, Calogero Zarcaro, Tommaso Vincenzo Bartolotta, Salvatore Gaglio, Salvatore Vitabile
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper presents a novel method, Rad4XCNN, to enhance the predictive power of CNN-derived features with radiomic features while achieving interpretability. The authors highlight the challenges of traditional explainability methods and propose a solution that does not compromise accuracy. They demonstrate the effectiveness of Rad4XCNN on breast cancer classification tasks using ultrasound imaging datasets. The method outperforms conventional visualization map methods, provides global explanations, and highlights the importance of proposing new methods for model explanation without affecting accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists developed a new way to make artificial intelligence (AI) models more understandable while keeping their ability to accurately predict medical conditions. They focused on breast cancer diagnosis using ultrasound images and showed that their approach is better than previous methods at explaining how AI decisions are made. This breakthrough could help doctors and patients understand the reasoning behind AI-assisted diagnoses, leading to improved healthcare. |
Keywords
» Artificial intelligence » Classification » Cnn
