Few-shot learning based histopathological image classification of colorectal cancer

Abstract

Background

Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic image classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning.

Methods

This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detailed the setup of hyperparameters: $n$-way, $k$-shot, $\beta$, and the creation of support, query, and test datasets.

Results

Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the $t$-SNE algorithm to analyze and assess the model’s classification performance.

Conclusion

The proposed model may demonstrate significant advantages in accuracy and minimal data dependency, performing robustly across all tested $n$-way, $k$-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.