Universal Semi-Supervised Learning for Medical Image Classification
This addresses a practical challenge in medical imaging by enabling SSL to work with diverse, real-world unlabeled data, though it is incremental as it builds on existing SSL and domain adaptation methods.
The paper tackles the problem of semi-supervised learning (SSL) in medical image classification when unlabeled data come from unseen classes or domains, proposing a unified framework that identifies and exploits such data to improve performance. It demonstrates superior classification results on dermatology and ophthalmology tasks.
Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution \textit{e.g.,} classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios. The code implementations are accessible at: https://github.com/PyJulie/USSL4MIC.