Summary of Self-supervised Radiograph Anatomical Region Classification — How Clean Is Your Real-world Data?, by Simon Langer et al.
Self-Supervised Radiograph Anatomical Region Classification – How Clean Is Your Real-World Data?
by Simon Langer, Jessica Ritter, Rickmer Braren, Daniel Rueckert, Paul Hager
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes novel deep learning-based methods to accurately identify anatomical regions in skeletal radiographs. The authors leverage self-supervised and supervised contrastive learning techniques, including SimCLR and BYOL, to classify the examined region into one of 14 categories. In-house datasets are used for training, with a single model achieving 96.6% linear evaluation accuracy and an ensemble approach yielding 97.7%. The study also investigates the effect of limited labeled instances (1% of the training set), demonstrating that even small amounts of labeled data can be sufficient for high-accuracy classification. Furthermore, the authors’ model can be used to correct data entry errors, improving labelling performance by up to 11 percentage points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand medical images like X-rays. The researchers created special computer models that can identify specific parts of the body in these images. They tested different methods and found that one way worked really well: it could correctly identify where in the body a bone was, even when only a few examples were labeled (like teachers giving students small assignments). This is important because medical data might be incorrectly labeled or missing labels. The models can correct these mistakes, making them more useful for doctors and scientists. |
Keywords
» Artificial intelligence » Classification » Deep learning » Self supervised » Supervised
