Summary of Boosting Medical Image-based Cancer Detection Via Text-guided Supervision From Reports, by Guangyu Guo et al.
Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports
by Guangyu Guo, Jiawen Yao, Yingda Xia, Tony C. W. Mok, Zhilin Zheng, Junwei Han, Le Lu, Dingwen Zhang, Jian Zhou, Ling Zhang
First submitted to arxiv on: 23 May 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 The proposed novel text-guided learning method leverages clinical reports and pre-trained vision-language models to enhance generalization ability and reduce human annotation efforts by at least 70% while maintaining comparable cancer detection accuracy to competing fully supervised methods. The approach utilizes a limited set of voxel-level tumor annotations and incorporates alongside a substantial number of medical images with only off-the-shelf clinical reports, striking a balance between minimizing expert annotation workload and optimizing screening efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to use medical imaging to detect cancer more accurately without needing as much human help. It uses special computer models that can look at both pictures and text. This helps the model learn what to look for in the pictures by using information from clinical reports. The model can then find tumors even when they’re small and hard to spot. |
Keywords
» Artificial intelligence » Generalization » Supervised
