Efficient Rare Disease Detection with Self-Supervised and Transfer Learning

Accurate diagnosis of rare diseases by analyzing medical images is often faced with the problem of limited availability of annotated datasets, as the manual annotation process, being time-consuming, requires expert knowledge in addition to a significant time investment.The research explores the use of Self-Supervised Learning (SSL) combined with Transfer Learning as a method to reduce the dependency on labeled data without compromising diagnostic performance.The approach designed involves pretraining convolutional neural networks using large unlabeled datasets through self-supervised learning (SSL) methods.For this purpose, the CheXpert dataset of over 200000 X-ray images is employed.To enable the model to imbibe generalizable features from the unlabeled pictures, several SSL pretext tasks are applied, namely rotation prediction, jigsaw puzzles, as well as contrastive learning (SimCLR).Following the pretraining process, the models are fine-tuned using the ISIC 2018 dataset, which contains annotated dermoscopic images for the task of skin lesion classification.Feature extraction and classification performance are evaluated using different architectures, such as ResNet-50.Finetuning is performed using different percentages of labeled data (10%, 25%, 50%, and 100%) to evaluate the efficiency of annotations in semi-supervised learning (SSL).The performance and models have been assessed with respect to Accuracy, Precision, Recall, and F1 Score.From the results, it appears that SSL-pretrained models using SimCLR outperformed fully supervised models even when they were trained on significantly fewer labeled samples.It is astonishing that models which were trained with only 50% of the labeled data actually achieved over 75% accuracy in comparison to those trained on the complete dataset.This work demonstrates that the semi-supervised learning (SSL) approach made possible through transfer learning is a highly effective and efficient in terms of labeling resources for detecting rare diseases in medical images.