Summary of Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection, by Yeonghyeon Park et al.
Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection
by YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A pre-trained visual-language model called Contrastive Language-Image Pre-training (CLIP) excels in various downstream tasks with text prompts, including finding images or localizing regions within the image. Despite its strong multi-modal data capabilities, CLIP remains limited in specialized environments like medical applications. To address this limitation, several CLIP variants have emerged, but false positives related to normal regions persist. Our goal is to reduce these false positives in medical anomaly detection by introducing a Contrastive LAnguage Prompting (CLAP) method that leverages both positive and negative text prompts. This straightforward approach identifies potential lesion regions by visual attention to the positive prompts in the given image, while attenuating attention on normal regions using negative prompts. Our experiments with the BMAD dataset demonstrate that the CLAP method enhances anomaly detection performance, outperforming existing methods on six biomedical benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A pre-trained model called Contrastive Language-Image Pre-training (CLIP) is good at completing tasks like finding images or localizing parts of an image when given text prompts. However, it’s not very good in medical applications. To fix this, people have created different versions of CLIP, but there are still some problems with false positives. We want to solve this problem by creating a new method that uses both positive and negative text prompts. This method helps identify potential problems in an image by paying attention to the right parts and ignoring normal areas. We tested our method on a dataset called BMAD and found that it works better than other methods on six medical tests. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Language model » Multi modal » Prompting
