Summary of Protoal: Interpretable Deep Active Learning with Prototypes For Medical Imaging, by Iury B. De A. Santos et al.
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
by Iury B. de A. Santos, André C.P.L.F. de Carvalho
First submitted to arxiv on: 6 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 The proposed ProtoAL method combines an interpretable deep learning model with the Deep Active Learning framework to address challenges in medical imaging-based Computer-aided diagnosis (AI-CAD) solutions. This approach aims to provide both interpretability and efficient data usage, utilizing prototypes as the foundation for its inherently interpretable model. The evaluation on the Messidor dataset yielded a precision-recall curve area under 0.79 while using only 76.54% of labeled data. This can enhance the practical usability of deep learning models in medical imaging applications, enabling trust calibration with domain experts and addressing data scarcity issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method for using artificial intelligence (AI) to help doctors analyze medical images. The current AI-based tools have limitations because they are hard to understand and require a lot of data. To solve these problems, the researchers created a new approach called ProtoAL. It combines an easy-to-understand AI model with another technique that helps learn from limited data. They tested this method on some medical image data and found it was quite accurate while only needing a small amount of labeled information. This could make AI more practical for use in hospitals, allowing doctors to trust the results and making it easier to work with limited data. |
Keywords
* Artificial intelligence * Active learning * Deep learning * Precision * Recall
