Open-Set Semi-Supervised Medical Image Classification with Learnable Prototypes and Outlier Filter

Semi-supervised learning (SSL) offers a pragmatic approach to harnessing unlabeled data, particularly in contexts where annotation costs are prohibitively high. However, in practical clinical settings, unlabeled datasets inevitably encompass outliers that do not align with labeled classes, constituting what is known as open-set Semi-supervised learning (OSSL). While existing methods have shown promising results in domains such as natural image processing, they often overlook the nuanced characteristics intrinsic to medical images, rendering them less applicable in this domain. In this work, we introduce a novel framework tailored for the nuanced challenges of open-set semi-supervised classification (OpenSSC) in medical imaging. OpenSSC comprises three integral components. Firstly, we propose the utilization of learnable prototypes to distill a compact representation of the fine-grained characteristics inherent in identified classes. Subsequently, a multi-binary discriminator is introduced to consolidate closed-set predictions and effectively delineate whether the sample belongs to its ground truth or not. Building upon these components, we present a joint outlier filter mechanism designed to classify known classes while discerning and identifying unknown classes within unlabeled datasets. Our proposed method demonstrates efficacy in handling open-set data. Extensive experimentation validates the effectiveness of our approach, showcasing superior performance compared to existing state-of-the-art methods in two distinct medical image classification tasks.