Summary of Learning General-purpose Biomedical Volume Representations Using Randomized Synthesis, by Neel Dey et al.
Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis
by Neel Dey, Benjamin Billot, Hallee E. Wong, Clinton J. Wang, Mengwei Ren, P. Ellen Grant, Adrian V. Dalca, Polina Golland
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed method addresses the limitation of current volumetric biomedical foundation models by developing a representation learning approach that anticipates strong domain shifts during training. A data engine synthesizes highly variable training samples to enable generalization to new biomedical contexts. A contrastive learning method pretrains a single 3D network to be stable against nuisance imaging variation, providing robust representations of input images and a strong initialization for finetuning on new datasets. This approach achieves state-of-the-art results in multimodality registration and few-shot segmentation without training on any existing dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach computers to recognize medical images from different sources, like MRI or CT scans. Right now, these computers struggle to work well with new types of images because they haven’t been trained on a wide variety of pictures. The authors of this paper came up with a way to fix this problem by creating a special kind of training data that helps the computer generalize to new situations. They also developed a special method for training the computer’s network, which allows it to be more robust and accurate when dealing with different types of images. This means the computer can learn to recognize medical images without needing a huge amount of training data. |
Keywords
» Artificial intelligence » Few shot » Generalization » Representation learning
