Summary of Revisiting Mae Pre-training For 3d Medical Image Segmentation, by Tassilo Wald et al.
Revisiting MAE pre-training for 3D medical image segmentation
by Tassilo Wald, Constantin Ulrich, Stanislav Lukyanenko, Andrei Goncharov, Alberto Paderno, Leander Maerkisch, Paul F. Jäger, Klaus Maier-Hein
First submitted to arxiv on: 30 Oct 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 The paper presents a self-supervised learning (SSL) approach for unlocking clinical datasets in 3D medical image computing, addressing three key pitfalls: small pre-training dataset sizes, inadequate architectures, and insufficient evaluation practices. The authors leverage a large-scale dataset of 39k 3D brain MRI volumes and use the nnU-Net framework with a Residual Encoder U-Net architecture. They develop a robust framework for optimizing Masked Auto Encoders (MAEs) for 3D CNNs, achieving state-of-the-art performance on 5 development and 8 testing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer learning to help doctors work better with medical images. It makes big improvements by using a lot of data and special computer tools. The results are really good and can be used for many different tasks. The paper’s main idea is to use computers to learn from pictures without needing humans to label them, which can take a long time. |
Keywords
* Artificial intelligence * Encoder * Self supervised
