Summary of An Organism Starts with a Single Pix-cell: a Neural Cellular Diffusion For High-resolution Image Synthesis, by Marawan Elbatel et al.
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis
by Marawan Elbatel, Konstantinos Kamnitsas, Xiaomeng Li
First submitted to arxiv on: 3 Jul 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 introduces a novel family of generative models, called Generative Cellular Automata (GeCAs), inspired by the evolution of an organism from a single cell. GeCAs are evaluated as an effective augmentation tool for retinal disease classification across two imaging modalities: Fundus and Optical Coherence Tomography (OCT). The authors show that GeCAs significantly boost the performance of 11 different ophthalmological conditions in OCT imaging, achieving a 12% increase in the average F1 score compared to conventional baselines. GeCAs outperform both diffusion methods that incorporate UNet or state-of-the-art variants with transformer-based denoising models, under similar parameter constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to create artificial data that’s similar to real data from the eye (retina). The method is inspired by how living things grow and develop. The authors test this approach on identifying diseases in retinal images and show it improves accuracy by 12%. This new technique beats other methods at doing the same job, even when they use more complex algorithms. |
Keywords
» Artificial intelligence » Classification » Diffusion » F1 score » Transformer » Unet
