More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data

TL;DR

This paper demonstrates that Compact Convolutional Transformers (CCT) can effectively classify medical images with limited data, achieving high accuracy and robustness compared to traditional transformers.

Contribution

The study introduces and validates the use of CCTs for medical image classification in data-scarce environments, showing their superior performance over conventional models.

Findings

Achieved 92.49% accuracy on blood cell classification

CCT learned quickly, reaching 80% accuracy after five epochs

Performance was strong across all cell types

Abstract

Transformers are very powerful tools for a variety of tasks across domains, from text generation to image captioning. However, transformers require substantial amounts of training data, which is often a challenge in biomedical settings, where high quality labeled data can be challenging or expensive to obtain. This study investigates the efficacy of Compact Convolutional Transformers (CCT) for robust medical image classification with limited data, addressing a key issue faced by conventional Vision Transformers - their requirement for large datasets. A hybrid of transformers and convolutional layers, CCTs demonstrate high accuracy on modestly sized datasets. We employed a benchmark dataset of peripheral blood cell images of eight distinct cell types, each represented by approximately 2,000 low-resolution (28x28x3 pixel) samples. Despite the dataset size being smaller than those typically used with Vision Transformers, we achieved a commendable classification accuracy of 92.49% and a micro-average ROC AUC of 0.9935. The CCT also learned quickly, exceeding 80% validation accuracy after five epochs. Analysis of per-class precision, recall, F1, and ROC showed that performance was strong across cell types. Our findings underscore the robustness of CCTs, indicating their potential as a solution to data scarcity issues prevalent in biomedical imaging. We substantiate the applicability of CCTs in data-constrained areas and encourage further work on CCTs.