Medi-CAT: Contrastive Adversarial Training for Medical Image Classification
This addresses the challenge of training deep learning models on small medical image datasets for improved classification, though it is incremental as it combines existing techniques.
The paper tackled the problem of underfitting and overfitting in medical image classification due to limited datasets by proposing Medi-CAT, a training strategy using pre-trained vision transformers with adversarial and contrastive learning, which improved accuracy by up to 2% on three benchmarks and up to 4.1% over baselines.
There are not many large medical image datasets available. For these datasets, 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 Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets. Specifically, the proposed training methodology employs large pre-trained vision transformers to overcome underfitting and adversarial and contrastive learning techniques to prevent overfitting. The proposed method is trained and evaluated on four medical image classification datasets from the MedMNIST collection. Our experimental results indicate that the proposed approach improves the accuracy up to 2% on three benchmark datasets compared to well-known approaches, whereas it increases the performance up to 4.1% over the baseline methods.