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Medi-CAT: Contrastive Adversarial Training for Medical Image Classification

Pervaiz Iqbal Khan; Adread Dengel; Sheraz Ahmed
In: Proceedings of ICAART 2024. International Conference on Agents and Artificial Intelligence (ICAART-2024), February 24-26, Rome, Italy, SCITEPRESS, 2024.


There are not many large medical image datasets available. Too small deep learning models can't learn useful features, so they don't work well due to underfitting, and too big models tend to overfit the limited data. As a result, there is a compromise between the two issues. This paper proposes a training strategy to overcome the aforementioned issues in medical imaging domain. Specifically, it employs a large pre-trained vision transformers to overcome underfitting and adversarial and contrastive learning techniques to prevent overfitting. The presented method has been trained and evaluated on four medical image classification datasets from the MedMNIST collection. Experimental results indicate the effectiveness of the method by improving the accuracy up-to 2% on three benchmark datasets compared to well-known approaches and up-to 4.1% over the baseline methods.