Overcoming the Small Dataset Challenge in Healthcare

Obtaining large datasets for training and evaluating models remains a challenge in the medical and healthcare fields due to patient privacy, organizational and legal constraints, and the complex, time-consuming nature of data collection from human subjects. Moreover, expert labeling of collected samples can be demanding, limiting the availability of labeled data. Training models on small datasets often results in low feasibility. Therefore, when increasing the dataset size through additional sample collection becomes unfeasible, alternative approaches should be considered. This paper offers an overview of strategies employed in the medical and healthcare domain to tackle small datasets or datasets with limited labeled samples, including data augmentation, transfer learning, and semi-supervised learning.