Summary of Dense Self-supervised Learning For Medical Image Segmentation, by Maxime Seince et al.
Dense Self-Supervised Learning for Medical Image Segmentation
by Maxime Seince, Loic Le Folgoc, Luiz Augusto Facury de Souza, Elsa Angelini
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Pix2Rep approach offers a self-supervised learning solution for few-shot medical image segmentation, addressing the challenge of high-quality annotations required by deep learning models. By leveraging unlabeled images and a novel pixel-level loss function, Pix2Rep reduces the annotation burden while achieving improved performance compared to existing semi- and self-supervised methods. The framework is applied to generic encoder-decoder backbones, such as U-Net, and demonstrates excellent results on cardiac MRI segmentation tasks. Key benefits include a 5-fold reduction in annotation requirements for equivalent performance and a 30% DICE improvement for one-shot segmentation under linear-probing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pix2Rep is an innovative way to make deep learning work better for medical image analysis without needing as much human help to label the images. It uses special math to learn patterns directly from unlabeled pictures, which can be useful when there aren’t many labeled examples available. The technique was tested on heart MRI images and showed significant improvement over other methods that require less human effort. |
Keywords
» Artificial intelligence » Deep learning » Encoder decoder » Few shot » Image segmentation » Loss function » One shot » Self supervised
