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

AI Article Synopsis

  • Labeling medical images takes a lot of time and money because it needs experts and many images to work well.
  • This study created a new learning approach using a model called SimCLR to help classify colorectal cancer types with less labeled data.
  • The new SimCLR model performed better than others, like those used by less experienced doctors, and it showed better groupings of different samples, making it easier to understand the results.

Article 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 kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522217PMC
http://dx.doi.org/10.1007/s10278-024-01123-9DOI Listing

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