Self-Supervised Multi-Scale Vision Transformer Framework for Early Detection and Segmentation of Brain Tumors in 3D MRI Volumes using Hybrid CNN-Transformer Architectures

Abstract

One of the most aggressive and lethal forms of neoplasia is brain tumor because they are typically diagnosed at an advanced stage, and there are inherent surgical complexities associated with the neuro-anatomic environment. To fill this critical diagnostic gap, the new methodology has been proposed that will be used to accurately and early segment tumor boundaries in 3D volumes of MRI. The framework is a Multi-Scale Self-Supervised Hybrid (MSSH) that was optimized in a unique way by hierarchical neural synthesis. Although recent developments have pursued 3D Self-Supervised Contrastive Learning to detect glioblastoma and Vision-Language Models to diagnose multimodally. The design explicitly targets the capture of the multifaceted topology of structural and reactive neuro-oncological features. A hybrid architecture, inspired by ensemble learning, is used to effectively combine the localized strength of Convolutional Neural Networks (CNNs) with the scale relational context of 3D Transformers. A self-trained Masked Voxel Reconstruction (MVR) protocol is used to attain a good balance between high-fidelity feature extraction and spatial performance. The method has been used to augment better precision to pathological limits by blending structural inductive biases and global attention mechanisms. Moreover, a multi-scale attention-based fusion framework enables the system to obtain context-related information at diverse resolutions, which is quite useful in classifying complex brain pathologies. The combination of these hybrid modules, combined with the resource efficient mixed-precision training, is what ensures that the system is not only explainable, but also fully deployable on commodity-grade clinical hardware. Quantitative validation is used to confirm the accuracy of the framework, with the Dice Similarity Coefficient (DSC) showing a value of 0.938 and Hausdorff Distance (HD95) at 2.84mm. Finally, the proposed system supports patient-specific planning of surgery and enhances the overall prognosis through the provision of a comprehensive volumetric analysis of the 3D tumor data.