Kidney Tumor Segmentation With a Multistage Adaptive Boundary‐Aware Network

Accurate kidney tumor segmentation is critical for surgical planning but is challenged by indistinct boundaries and high morphological variability in computed tomography (CT) images. We propose the adaptive boundary-aware network (MABS-Net). The architecture integrates three core innovations: (1) a boundary-aware multiscale feature extraction module using learnable boundary-enhancing convolutions and adaptive weight maps to capture subtle edge cues; (2) an adaptive three-stage cascaded strategy