What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Various trending image generative techniques, such as diffusion models, have enabled visually appealing outcomes with just text-based descriptions. Unlike general images, where assessing the quality and alignment with text descriptions is trivial, establishing such a relation in a clinical setting proves challenging. This work investigates various strategies to evaluate the clinical significance of synthetic polyp images of different pathologies. We further explore if a relation could be established between qualitative results and their clinical relevance.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 4 of its 10 references that are in the map. First posted on arXiv 14 Jul 2024.
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- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2016A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Imagescited by 220
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- 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
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- 2024Comparative Analysis of Diffusion Generative Models in Computational Pathology