- Sandesh Pokhrel15,
- Sanjay Bhandari15,
- Eduard Vazquez16,
- Tryphon Lambrou18,
- Prashnna Gyawali17 &
- …
- Binod Bhattarai15,18
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 15265))
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Abstract
Deep learning has significantly advanced the field of gastrointestinal vision, enhancing disease diagnosis capabilities. One major challenge in automating diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic images. Due to the sparsity of data, this process of distinguishing normal from abnormal cases has faced significant challenges, particularly with rare and unseen conditions. To address this issue, we frame abnormality detection as an out-of-distribution (OOD) detection problem. In this setup, a model trained on In-Distribution (ID) data, which represents a healthy GI tract, can accurately identify healthy cases, while abnormalities are detected as OOD, regardless of their class. We introduce a test-time augmentation segment into the OOD detection pipeline, which enhances the distinction between ID and OOD examples, thereby improving the effectiveness of existing OOD methods with the same model. This augmentation shifts the pixel space, which translates into a more distinct semantic representation for OOD examples compared to ID examples. We evaluated our method against existing state-of-the-art OOD scores, showing improvements with test-time augmentation over the baseline approach.
S. Pokhrel and S. Bhandari—Equal Contribution.
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Authors and Affiliations
Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Lalitpur, Nepal
Sandesh Pokhrel, Sanjay Bhandari & Binod Bhattarai
Fogsphere (Redev AI Ltd.), 64 Southwark Bridge Road, London, SE1 0AS, UK
Eduard Vazquez
West Virginia University, Morgantown, USA
Prashnna Gyawali
School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK
Tryphon Lambrou & Binod Bhattarai
- Sandesh Pokhrel
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- Sanjay Bhandari
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- Tryphon Lambrou
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- Prashnna Gyawali
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- Binod Bhattarai
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Correspondence toBinod Bhattarai.
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Editors and Affiliations
University of Aberdeen, Aberdeen, UK
Binod Bhattarai
University of Leeds, Leeds, UK
Sharib Ali
Stanford University, Stanford, CA, USA
Anita Rau
University College London, London, UK
Razvan Caramalau
University of Liverpool, Liverpool, UK
Anh Nguyen
West Virginia University, Morgantown, WV, USA
Prashnna Gyawali
University of Oxford, Oxford, UK
Ana Namburete
University College London, London, UK
Danail Stoyanov
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Pokhrel, S., Bhandari, S., Vazquez, E., Lambrou, T., Gyawali, P., Bhattarai, B. (2025). TTA-OOD: Test-Time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision. In: Bhattarai, B.,et al. Data Engineering in Medical Imaging. DEMI 2024. Lecture Notes in Computer Science, vol 15265. Springer, Cham. https://doi.org/10.1007/978-3-031-73748-0_4
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