Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models Article Swipe
Tianyi Ren
,
Abhishek Sharma
,
Juampablo Heras Rivera
,
Harshitha Rebala
,
Ethan Honey
,
Agamdeep Chopra
,
Mehmet Kurt
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.07354
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.07354
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the years, creating a fully autonomous system suitable for clinical floors remains a formidable challenge because the model predictions have not yet reached the desired level of accuracy and generalizability for clinical applications. Generative modeling techniques have seen significant improvements in recent times. Specifically, Generative Adversarial Networks (GANs) and Denoising-diffusion-based models (DDPMs) have been used to generate higher-quality images with fewer artifacts and finer attributes. In this work, we introduce a framework called Re-Diffinet for modeling the discrepancy between the outputs of a segmentation model like U-Net and the ground truth, using DDPMs. By explicitly modeling the discrepancy, the results show an average improvement of 0.55\% in the Dice score and 16.28\% in HD95 from cross-validation over 5-folds, compared to the state-of-the-art U-Net segmentation model.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.07354
- https://arxiv.org/pdf/2402.07354
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391801022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391801022Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.07354Digital Object Identifier
- Title
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Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsWork title
- Type
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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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2024-02-12Full publication date if available
- Authors
-
Tianyi Ren, Abhishek Sharma, Juampablo Heras Rivera, Harshitha Rebala, Ethan Honey, Agamdeep Chopra, Mehmet KurtList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.07354Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2402.07354Direct 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/2402.07354Direct OA link when available
- Concepts
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Diffusion, Segmentation, Computer science, Artificial intelligence, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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