Summary of Siamese Networks with Soft Labels For Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms, by Kevin Van Vorst and Li Shen
Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms
by Kevin Van Vorst, Li Shen
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes an alternative self-supervised learning method for pretraining deep learning models to perform downstream tasks in medical imaging. Unlike most existing methods developed on large-scale image datasets, this approach uses contralateral mammograms to learn embeddings that can distinguish abnormal lesions from background tissues in a fully unsupervised manner. The method incorporates soft labels derived from the Euclidean distances between the embeddings of image pairs into the Siamese network loss, demonstrating superior performance in mammogram patch classification compared to existing self-supervised learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help doctors diagnose diseases earlier and more accurately. Right now, AI systems are mostly trained on pictures of natural objects like animals or buildings. But medical images, like X-rays and MRIs, are different because they often show blurry or unclear areas. The researchers in this study came up with a new way to train AI systems using mammography images, which can help doctors detect breast cancer earlier. They tested their method on many different images and found that it works better than other AI methods. |
Keywords
* Artificial intelligence * Classification * Deep learning * Pretraining * Self supervised * Siamese network * Unsupervised
