Tumor Sub Region

Tumor sub-region analysis focuses on identifying and characterizing distinct areas within tumors, aiming to improve cancer diagnosis, treatment planning, and prognosis prediction. Current research heavily utilizes deep learning models, including U-Net variations, ResNets, and Vision Transformers, often incorporating attention mechanisms and multi-modal image data (e.g., MRI, PET/CT, fluorescence microscopy) to achieve accurate segmentation and classification of these sub-regions. This work is significant because it allows for a more precise understanding of tumor heterogeneity and its impact on patient outcomes, potentially leading to personalized medicine approaches and improved diagnostic tools.