Summary of Latent Drifting in Diffusion Models For Counterfactual Medical Image Synthesis, by Yousef Yeganeh et al.
Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis
by Yousef Yeganeh, Ioannis Charisiadis, Marta Hasny, Martin Hartenberger, Björn Ommer, Nassir Navab, Azade Farshad, Ehsan Adeli
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the limitations of training diffusion models on large datasets, which is crucial for applications such as synthetic sample generation where real data is scarce. Specifically, it proposes Latent Drift (LD) to mitigate distribution shift issues between pre-trained general models and medical images. LD enables conditioned image generation for complex tasks like counterfactual image generation, allowing parameters like gender, age, or adding/removing diseases in a patient to be altered. The method is evaluated on three public longitudinal benchmark datasets of brain MRI and chest X-rays, demonstrating significant performance gains when combined with different fine-tuning schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better fake medical images that look real. It’s hard to get big datasets for medical imaging because it costs a lot and raises privacy concerns. To solve this problem, the researchers developed a new technique called Latent Drift (LD). LD lets computers generate realistic medical images based on existing information. This is important for studying how certain factors like age or disease can change medical images. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Image generation
