Summary of Block Expanded Dinoret: Adapting Natural Domain Foundation Models For Retinal Imaging Without Catastrophic Forgetting, by Jay Zoellin et al.
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting
by Jay Zoellin, Colin Merk, Mischa Buob, Amr Saad, Samuel Giesser, Tahm Spitznagel, Ferhat Turgut, Rui Santos, Yukun Zhou, Sigfried Wagner, Pearse A. Keane, Yih Chung Tham, Delia Cabrera DeBuc, Matthias D. Becker, Gabor M. Somfai
First submitted to arxiv on: 25 Sep 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 The paper explores the integration of deep learning into medical imaging for diagnostic purposes, focusing on foundation models and self-supervised learning. It addresses challenges in generalizability by using natural domain foundation models and proposes a novel strategy called block expansion to mitigate catastrophic forgetting during fine-tuning. The authors develop two foundation models, DINORET and BE DINORET, for retinal imaging classification tasks, demonstrating competitive performance with state-of-the-art models like RETFound. The study highlights the potential of self-supervised learning and block expansion in enabling healthcare institutions to develop tailored vision models for their patient populations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computer technology to help doctors make better diagnoses from medical images. It tries to solve a problem called “generalizability” by creating special kinds of models that can learn from lots of different images. The authors test these models on pictures of eyes and find that they work really well for diagnosing diseases like diabetic retinopathy and glaucoma. They also come up with a new way to make sure the models don’t forget what they learned before, which is important because doctors need to be able to use their knowledge to help patients. |
Keywords
» Artificial intelligence » Classification » Deep learning » Fine tuning » Self supervised
