  Wireless capsule endoscopy (WCE) is a non-invasive diagnostic procedure that
enables visualization of the gastrointestinal (GI) tract. Deep learning-based
methods have shown effectiveness in disease screening using WCE data,
alleviating the burden on healthcare professionals. However, existing capsule
endoscopy classification methods mostly rely on pre-defined categories, making
it challenging to identify and classify out-of-distribution (OOD) data, such as
undefined categories or anatomical landmarks. To address this issue, we propose
the Endoscopy Out-of-Distribution (EndoOOD) framework, which aims to
effectively handle the OOD detection challenge in WCE diagnosis. The proposed
framework focuses on improving the robustness and reliability of WCE diagnostic
capabilities by incorporating uncertainty-aware mixup training and long-tailed
in-distribution (ID) data calibration techniques. Additionally, virtual-logit
matching is employed to accurately distinguish between OOD and ID data while
minimizing information loss. To assess the performance of our proposed
solution, we conduct evaluations and comparisons with 12 state-of-the-art
(SOTA) methods using two publicly available datasets. The results demonstrate
the effectiveness of the proposed framework in enhancing diagnostic accuracy
and supporting clinical decision-making.
