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TTA-OOD: Test-Time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision

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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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Author information

Authors and Affiliations

  1. Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Lalitpur, Nepal

    Sandesh Pokhrel, Sanjay Bhandari & Binod Bhattarai

  2. Fogsphere (Redev AI Ltd.), 64 Southwark Bridge Road, London, SE1 0AS, UK

    Eduard Vazquez

  3. West Virginia University, Morgantown, USA

    Prashnna Gyawali

  4. School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK

    Tryphon Lambrou & Binod Bhattarai

Authors
  1. Sandesh Pokhrel

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  2. Sanjay Bhandari

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  3. Eduard Vazquez

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  4. Tryphon Lambrou

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  5. Prashnna Gyawali

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  6. Binod Bhattarai

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Corresponding author

Correspondence toBinod Bhattarai.

Editor information

Editors and Affiliations

  1. University of Aberdeen, Aberdeen, UK

    Binod Bhattarai

  2. University of Leeds, Leeds, UK

    Sharib Ali

  3. Stanford University, Stanford, CA, USA

    Anita Rau

  4. University College London, London, UK

    Razvan Caramalau

  5. University of Liverpool, Liverpool, UK

    Anh Nguyen

  6. West Virginia University, Morgantown, WV, USA

    Prashnna Gyawali

  7. University of Oxford, Oxford, UK

    Ana Namburete

  8. 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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