A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

ABSTRACT: Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kap

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11522217 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature