Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
For medical imaging researchers, this work addresses the false negative problem in contrastive learning, showing strong gains on a specific pediatric brain tumor classification task.
The paper proposes Multimodal Semantic-Aware Contrastive Learning (MseaCL) to mitigate false negatives in multimodal contrastive learning for 3D brain MRI and radiology reports, achieving at least a 22.6% AUC improvement in pediatric brain tumor molecular classification.
Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare. It works by minimizing the distance between matched (positive) data modalities, while maximizing the distance between mismatched (negative) samples. Traditional CL frameworks typically assume instance-based correspondence within data batches, treating all non-paired samples as negatives. However, this assumption often fails in medical settings, where samples may share high-level semantic attributes, leading to false negatives that degrade representation quality. In this paper, we propose Multimodal Semantic-Aware Contrastive Learning (MseaCL), a CL framework trained on a pediatric cohort of 3D brain magnetic resonance imaging (MRI) scans and radiology reports. The goal of this framework is to mitigate the impact of semantically similar false negative samples by incorporating semantic similarity between radiology reports, as a guiding signal during the learning process. Our results indicate that applying this framework as a pretraining stage can achieve notable improvements in downstream tasks, e.g., at least a 22.6\% increase in the area under the receiver operating characteristic curve (AUC) of pediatric brain tumor molecular classification, demonstrating its potential for more robust and semantically aligned multimodal representations in clinical applications.