Summary of Learning a Clinically-relevant Concept Bottleneck For Lesion Detection in Breast Ultrasound, by Arianna Bunnell et al.
Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound
by Arianna Bunnell, Yannik Glaser, Dustin Valdez, Thomas Wolfgruber, Aleen Altamirano, Carol Zamora González, Brenda Y. Hernandez, Peter Sadowski, John A. Shepherd
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 proposed explainable AI model for detecting and classifying lesions in breast ultrasound images is a deep neural network that features a concept bottleneck layer, predicting known BI-RADS features before making a final cancer classification. This enables radiologists to review predictions and potentially fix errors in real-time by modifying the concept predictions. The model outperforms state-of-the-art lesion detection frameworks with 48.9 average precision on the held-out testing set, and concept intervention increases performance for cancer classification from 0.876 to 0.885 area under the receiver operating characteristic curve. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps radiologists detect breast lesions better by using a special type of AI model that explains its predictions. This makes it easier to review and correct any mistakes. The researchers developed this model on a large set of ultrasound images with expert annotations, and it works better than other similar models. This can help reduce the burden of cancer in regions where mammography is not widely available. |
Keywords
* Artificial intelligence * Classification * Neural network * Precision
