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

Objective Colorectal 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. Methods This 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-nearest neighbor classification. Results Evaluated across three rounds, the few-shot learning model outperformed conventional deep learning models and both junior and senior endoscopists in several metrics, achieving an average macro–area under the curve of 0.731, macro–F1-score of 0.674, Matthews correlation coefficient of 0.526, and Cohen’s kappa of 0.523. When identifying neuroendocrine tumors specifically, the model achieved the highest accuracy (0.823), sensitivity (0.653), precision (0.673), and F1-score (0.659). Conclusions The few-shot learning approach effectively addresses data scarcity issues and improves diagnostic accuracy, offering a promising tool for computer-aided diagnosis of rare gastrointestinal diseases.