Summary of Self-supervised Vision-langage Alignment Of Deep Learning Representations For Bone X-rays Analysis, by Alexandre Englebert et al.
Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis
by Alexandre Englebert, Anne-Sophie Collin, Olivier Cornu, Christophe De Vleeschouwer
First submitted to arxiv on: 14 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 paper develops a novel approach to address downstream tasks in bone radiography by leveraging vision-language pretraining on paired bone X-rays and French medical reports. The method involves anonymizing and processing French medical reports, followed by self-supervised alignment of visual and textual embedding spaces using deep model encoders. The resulting image encoder is then applied to various downstream tasks, including quantification of osteoarthritis, estimation of bone age, bone fracture detection, and anomaly detection. Experimental results demonstrate competitive performance compared to alternatives requiring more human expert annotations. This work contributes to the deployment of vision models in wider healthcare applications by capitalizing on large datasets of paired images and reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computers to look at X-ray pictures of bones and match them with doctor’s notes written in French. It helps the computer understand what it sees in the X-rays better, which can be useful for diagnosing things like osteoarthritis or bone fractures. The computer is able to do this without needing a lot of help from doctors, making it easier to use in hospitals. This is important because there are many X-ray pictures and notes that computers can learn from, and this can help make medicine more efficient. |
Keywords
» Artificial intelligence » Alignment » Anomaly detection » Encoder » Pretraining » Self supervised
