Summary of A Concept-based Interpretable Model For the Diagnosis Of Choroid Neoplasias Using Multimodal Data, by Yifan Wu et al.
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
by Yifan Wu, Yang Liu, Yue Yang, Michael S. Yao, Wenli Yang, Xuehui Shi, Lihong Yang, Dongjun Li, Yueming Liu, James C. Gee, Xuan Yang, Wenbin Wei, Shi Gu
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 Machine learning educators can appreciate the significance of diagnosing rare diseases in clinical practice. The scarcity of data on rare conditions hinders the development of interpretable and trustworthy models. Interpretable AI, with its human-readable outputs, can facilitate validation by clinicians and contribute to medical education. A recent study introduces a concept-based interpretable model that distinguishes between three types of choroidal tumors, integrating insights from domain experts via radiological reports. The model achieves an F1 score of 0.91, rivaling black-box models, and boosts the diagnostic accuracy of junior doctors by 42%. This study highlights the potential of interpretable machine learning in improving the diagnosis of rare diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rare diseases are hard to diagnose, which makes it important for doctors to be good at recognizing them. Machine learning can help, but we need more data on rare conditions and models that are easy to understand. One way to make AI models easier to use is by making them interpretable, so doctors can see how they work and why they made certain decisions. A new study has developed a model that can identify three types of eye cancer, using reports from experts in the field. This model is very good at identifying these cancers, even better than some other models that are not as easy to understand. The study shows that this kind of AI can help doctors diagnose rare diseases more accurately. |
Keywords
* Artificial intelligence * F1 score * Machine learning
