Summary of Dino-diffusion. Scaling Medical Diffusion Via Self-supervised Pre-training, by Guillermo Jimenez-perez et al.
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training
by Guillermo Jimenez-Perez, Pedro Osorio, Josef Cersovsky, Javier Montalt-Tordera, Jens Hooge, Steffen Vogler, Sadegh Mohammadi
First submitted to arxiv on: 16 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 This paper introduces DiNO-Diffusion, a self-supervised method for training latent diffusion models (LDMs) that conditions the generation process on image embeddings extracted from DiNO. Unlike traditional diffusion models, which require large annotated datasets, DiNO-Diffusion leverages over 868k unlabelled images from public chest X-Ray (CXR) datasets. The model shows comprehensive manifold coverage, with FID scores as low as 4.7, and emerging properties when evaluated in downstream tasks. DiNO-Diffusion can be used to generate semantically-diverse synthetic datasets even from small data pools, demonstrating up to 20% AUC increase in classification performance when used for data augmentation. The model also demonstrates zero-shot segmentation performance of up to 84.4% Dice score when evaluating lung lobe segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train computers to generate images, called DiNO-Diffusion. It’s special because it doesn’t need lots of labeled pictures to learn. Instead, it uses millions of unlabeled images from the internet. This makes it useful for medical imaging, where there are limited amounts of data. The computer can even create new synthetic images that are similar to real X-rays, which helps with diagnosing diseases. It’s also good at recognizing specific parts of the body, like lungs. Overall, DiNO-Diffusion is a powerful tool for generating images and could help doctors diagnose patients more accurately. |
Keywords
» Artificial intelligence » Auc » Classification » Data augmentation » Diffusion » Self supervised » Zero shot
