Few-shot learning for the classification of colorectal neuroendocrine tumors and polyps on endoscopic images.

ABSTRACT: ObjectiveColorectal neuroendocrine tumors and polyps share similar endoscopic features, often resulting in misdiagnosis. As neuroendocrine tumors are rare, obtaining a sufficient number of images for deep learning models is challenging.MethodsThis study introduces a few-shot learning model for ternary classification of neuroendocrine tumors, serrated lesions and polyps, and traditional adenomas using endoscopic images. Three groups of images (56 serrated lesions and polyps, 86 adenomas, and 53 neuroendocrine tumors) were collected retrospectively and divided into Support Sets and Query Sets. The proposed few-shot learning model involved transfer learning using ResNet50 V2 pretrained on ImageNet and esophageal endoscopic images, followed by metric learning based on Euclidean distances and K

SUBMITTER: Zhu S 

PROVIDER: S-EPMC12644450 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature