Self-supervised learning for skin cancer diagnosis with limited training data

• URL: http://arxiv.org/abs/2401.00692v3
• Date: Tue, 26 Nov 2024 05:29:17 GMT
• Title: Self-supervised learning for skin cancer diagnosis with limited training data
• Authors: Hamish Haggerty, Rohitash Chandra,
• Abstract summary: Self-supervised learning (SSL) is an alternative to the standard supervised pre-training on ImageNet for scenarios with limited training data. We consider textitfurther SSL pre-training on task-specific datasets, where our implementation is motivated by supervised transfer learning. We find minimal further SSL pre-training on task-specific data can be as effective as large-scale SSL pre-training on ImageNet for medical image classification tasks with limited labelled data.
• Score: 0.196629787330046
• License:
• Abstract: Early cancer detection is crucial for prognosis, but many cancer types lack large labelled datasets required for developing deep learning models. This paper investigates self-supervised learning (SSL) as an alternative to the standard supervised pre-training on ImageNet for scenarios with limited training data using a deep learning model (ResNet-50). We first demonstrate that SSL pre-training on ImageNet (via the Barlow Twins SSL algorithm) outperforms supervised pre-training (SL) using a skin lesion dataset with limited training samples. We then consider \textit{further} SSL pre-training (of the two ImageNet pre-trained models) on task-specific datasets, where our implementation is motivated by supervised transfer learning. This approach significantly enhances initially SL pre-trained models, closing the performance gap with initially SSL pre-trained ones. Surprisingly, further pre-training on just the limited fine-tuning data achieves this performance equivalence. Linear probe experiments reveal that improvement stems from enhanced feature extraction. Hence, we find that minimal further SSL pre-training on task-specific data can be as effective as large-scale SSL pre-training on ImageNet for medical image classification tasks with limited labelled data. We validate these results on an oral cancer histopathology dataset, suggesting broader applicability across medical imaging domains facing labelled data scarcity.

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