CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior Article Swipe
Yongyi Shi
,
Chuang Niu
,
Amber L. Simpson
,
Bruno De Man
,
Richard Kinh Gian
,
Ge Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.00092
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.00092
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative diffusion prior to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.00092
- https://arxiv.org/pdf/2408.00092
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401306966
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401306966Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.00092Digital Object Identifier
- Title
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CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion PriorWork 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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2024-07-31Full publication date if available
- Authors
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Yongyi Shi, Chuang Niu, Amber L. Simpson, Bruno De Man, Richard Kinh Gian, Ge WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.00092Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2408.00092Direct link to full text PDF
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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/2408.00092Direct OA link when available
- Concepts
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Anomaly (physics), Generative grammar, Anomaly detection, Computer science, Artificial intelligence, Radiology, Medicine, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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