NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
The integration of deep learning tools in gastrointestinal vision holds the
potential for significant advancements in diagnosis, treatment, and overall
patient care. A major challenge, however, is these tools' tendency to make
overconfident predictions, even when encountering unseen or newly emerging
disease patterns, undermining their reliability.
We address this critical issue of reliability by framing it as an
out-of-distribution (OOD) detection problem, where previously unseen and
emerging diseases are identified as OOD examples. However, gastrointestinal
images pose a unique challenge due to the overlapping feature representations
between in- Distribution (ID) and OOD examples. Existing approaches often
overlook this characteristic, as they are primarily developed for natural image
datasets, where feature distinctions are more apparent. Despite the overlap, we
hypothesize that the features of an in-distribution example will cluster closer
to the centroids of their ground truth class, resulting in a shorter distance
to the nearest centroid. In contrast, OOD examples maintain an equal distance
from all class centroids. Based on this observation, we propose a novel
nearest-centroid distance deficit (NCCD) score in the feature space for
gastrointestinal OOD detection.
Evaluations across multiple deep learning architectures and two publicly
available benchmarks, Kvasir2 and Gastrovision, demonstrate the effectiveness
of our approach compared to several state-of-the-art methods. The code and
implementation details are publicly available at:
https://github.com/bhattarailab/NCDD
