Summary of An Openmind For 3d Medical Vision Self-supervised Learning, by Tassilo Wald and Constantin Ulrich and Jonathan Suprijadi and Michal Nohel and Robin Peretzke and Klaus H. Maier-hein
An OpenMind for 3D medical vision self-supervised learning
by Tassilo Wald, Constantin Ulrich, Jonathan Suprijadi, Michal Nohel, Robin Peretzke, Klaus H. Maier-Hein
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 aims to standardize the field of 3D medical vision self-supervised learning by introducing a large publicly available pre-training dataset and benchmarking existing methods under common architectures. The proposed framework is designed to facilitate method advancements and rapid adoption, with the goal of identifying the current state-of-the-art in this area. The authors also provide an open-source codebase for reproducing their results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps clarify the field by providing a large dataset and benchmarking existing self-supervised learning methods. This allows researchers to identify the current best practices and compare different approaches. The goal is to advance the development of 3D medical vision techniques and improve their accuracy. |
Keywords
» Artificial intelligence » Self supervised
