Comparative Analysis of Diffusion Generative Models in Computational Pathology Article Swipe
Denisha Thakkar
,
Vincent Quoc‐Huy Trinh
,
Sonal Varma
,
Samira Ebrahimi Kahou
,
Hassan Rivaz
,
Mahdi S. Hosseini
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.15719
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.15719
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning applications. Despite their high computational demand, these models are extensively utilized for their superior sample quality and robust mode coverage. While research in diffusion generative models is advancing, exploration within the domain of computational pathology and its large-scale datasets has been comparatively gradual. Bridging the gap between the high-quality generation capabilities of Diffusion Generative Models and the intricate nature of pathology data, this paper presents an in-depth comparative analysis of diffusion methods applied to a pathology dataset. Our analysis extends to datasets with varying Fields of View (FOV), revealing that DGMs are highly effective in producing high-quality synthetic data. An ablative study is also conducted, followed by a detailed discussion on the impact of various methods on the synthesized histopathology images. One striking observation from our experiments is how the adjustment of image size during data generation can simulate varying fields of view. These findings underscore the potential of DGMs to enhance the quality and diversity of synthetic pathology data, especially when used with real data, ultimately increasing accuracy of deep learning models in histopathology. Code is available from https://github.com/AtlasAnalyticsLab/Diffusion4Path
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.15719
- https://arxiv.org/pdf/2411.15719
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404986738Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.15719Digital Object Identifier
- Title
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Comparative Analysis of Diffusion Generative Models in Computational PathologyWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-24Full publication date if available
- Authors
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Denisha Thakkar, Vincent Quoc‐Huy Trinh, Sonal Varma, Samira Ebrahimi Kahou, Hassan Rivaz, Mahdi S. HosseiniList of authors in order
- Landing page
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https://arxiv.org/abs/2411.15719Publisher landing page
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https://arxiv.org/pdf/2411.15719Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2411.15719Direct OA link when available
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Generative grammar, Diffusion, Computer science, Pathology, Computational biology, Medicine, Artificial intelligence, Biology, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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