Medical imaging analysis plays a pivotal role in modern healthcare, with physicians relying heavily on radiologists for disease diagnosis. However, many hospitals face a shortage of radiologists, leading to long queues at radiology centers and delays in diagnosis. Advances in artificial intelligence (AI) have made it possible for AI models to analyze medical images and provide insights similar to those of radiologists. Despite their successes, these models face significant challenges that hinder widespread adoption. One major issue is the inability of AI models to generalize data from new populations, as performance tends to degrade when evaluated on datasets with different or shifted distributions, a problem known as domain shift. Additionally, the large size of these models requires substantial computational resources for training and deployment. In this study, we address these challenges by investigating domain shifts using ChestXray-14 and a Nigerian chest X-ray dataset. We propose a multi-task learning (MTL) approach that jointly trains the model on both datasets for two tasks, classification and segmentation, to minimize the domain gap. Furthermore, we replace traditional convolutional layers in the backbone model (Densenet-201) architecture with depthwise separable convolutions, reducing the model's number of parameters and computational requirements. Our proposed model demonstrated remarkable improvements in both accuracy and AUC, achieving 93% accuracy and 96% AUC when tested across both datasets, significantly outperforming traditional transfer learning methods.
Musa et al. (Fri,) studied this question.