Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning

Brain tumors have a great impact on patients’ quality of life and accurate histopathological classification of brain tumors is crucial for patients’ prognosis. Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide images (WSIs). However, current MIL-based methods face several issues, including significant redundancy in the input and feature space, insufficient modeling of spatial relations between patches and inadequate representation capability of the feature extractor. To solve these limitations, we propose a new multi-instance learning with weakly supervised contrastive learning for brain tumor classification. Our framework consists of two parts: a cross-detection MIL aggregator (CDMIL) for brain tumor classification and a contrastive learning model based on pseudo-labels (PSCL) for optimizing feature encoder. The CDMIL consists of three modules: an internal patch anchoring module (IPAM), a local structural learning module (LSLM) and a cross-detection module (CDM). Specifically, IPAM utilizes probability distribution to generate representations of anchor samples, while LSLM extracts representations of local structural information between anchor samples. These two representations are effectively fused in CDM. Additionally, we propose a bag-level contrastive loss to interact with different subtypes in the feature space. PSCL uses the samples and pseudo-labels anchored by IPAM to optimize the performance of the feature encoder, which extracts a better feature representation to train CDMIL. We performed benchmark tests on a self-collected dataset and a publicly available dataset. The experiments show that our method has better performance than several existing state-of-the-art methods.