Transfer Learning for Medical Image Classification on Multiple Datasets using PubMedCLIP

Transfer learning is a key solution to deal with the problem of data scarcity, where learning process leverages knowledge learned from similar tasks. In this study, we employ PubMedCLIP as a pre-trained model for the medical image classification tasks. We evaluate multiple datasets of different body regions rather simultaneously. Moreover, we investigate different amounts of training data to evaluate the effectiveness of PubMedCLIP in the case of limited training data. The experimental results show that transfer learning using PubMedCLIP can achieve good results in across a diverse range of body-region datasets. To the best of our knowledge, this is the first study that employs PubMedCLIP for transfer learning.