# Big Self-Supervised Models Advance Medical Image Classification Source: [Big Self-Supervised Models Advance Medical Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:05:39.232Z Authors: Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi Published: 2021-01-13T17:36:31.000Z ## Abstract Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology skin condition classification from digital camera images and multi-label chest X-ray classification, and demonstrate that self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled domain-specific medical images significantly improves the accuracy of medical image classifiers. We introduce a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case, when available, to construct more informative positive pairs for self-supervised learning. Combining our contributions, we achieve an improvement of 6.7% in top-1 accuracy and an improvement of 1.1% in mean AUC on dermatology and chest X-ray classification respectively, outperforming strong supervised baselines pretrained on ImageNet. In addition, we show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [COVID-19 Ventilation Prediction on SnapCXR (test)](): COVID-19 Ventilation Prediction; SnapCXR (test); AUC: 0.79. - [COVID-19 Mortality Prediction on SnapCXR dataset (test)](): COVID-19 Mortality Prediction; SnapCXR dataset (test); AUC: 80. - [Pathological Image Classification on TCGA melanoma (test)](): Pathological image classification; TCGA melanoma (test); F1 Score: 0.5856. ## Original sources and implementation [Original paper]() [PDF]()