BeViL: Brain Tumor Detection Beyond Visible Lesions Using Secondary Effects via Contrastive Loss

Accurate brain tumor detection from Magnetic Resonance Imaging (MRI) remains challenging when lesions are subtle, partially visible, or missed due to imaging artifacts, sparse slice acquisition, and reconstruction errors. Most existing methods primarily rely on visible lesion appearance and feature fusion across modalities, which require high-resolution, artifact-free, and multimodal 3D MRI acquisitions, which are often time-consuming and costly. To address this limitation, we introduce a novel problem formulation for brain tumor detection that relies solely on the tumor-induced secondary effects present in neighboring slices. Using paired FLAIR, T1-weighted, and T2-weighted MRI, modality-specific DenseNet201 classifiers are trained with a joint objective that combines cross-entropy loss and supervised contrastive loss. The contrastive objective aligns class-consistent representations across modalities, enabling effective representation learning by encouraging shared discriminative features that capture indirect tumor-related cues beyond explicit lesion visibility. During inference, each modality is employed independently. Experimental results demonstrate that the proposed framework consistently outperforms single-modality baselines and non-contrastive multimodal training strategies. Using only secondary tumor-induced effects rather than explicit lesion appearance, the proposed approach reduces the risk of misdiagnosis in the case of sparse acquisitions and can be extended to more reliable and early detection of brain tumors.