MDA-Net: A Segmentation Network for Kidney Tumor Based on Enhanced Multi-Scale Feature Extraction and Attention Refinement

Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still struggle with multi-scale tumor characteristics, complex morphological variations, and background noise in medical images. To address these challenges, we propose MDA-Net, an end-to-end segmentation method based on enhanced multi-scale feature extraction and attention refinement. Specifically, we introduce a Multi-Scale Feature Extraction (MSFE) module into encoder–decoder skip connections to aggregate dilated features across multiple receptive fields and learn branch-wise weights for adaptive refinement and fusion, thereby enhancing boundary details and semantic cues to reduce tumor-tissue ambiguity. At the bottleneck, a Deformable Pyramid Feature Refinement (DPFR) module combines deformable sampling with pyramid contextual modeling, thereby improving adaptability to variations in tumor shape and scale while preserving feature resolution. Moreover, a Channel and Spatial Attention (CASA) module is embedded in the decoder to suppress background interference and enhance boundary-sensitive structures during upsampling via coordinated channel and spatial reweighting, thereby improving the reconstruction of fine-grained tumor morphology and contours. Experiments on both KiTS19 and KiTS21 show that MDA-Net consistently improves tumor boundary delineation, lesion localization, and mask reconstruction, demonstrating stronger robustness and cross-dataset generalizability than representative baseline methods. Ablation studies further confirm the complementary effects of MSFE, DPFR, and CASA. In addition, Grad-CAM visualizations improve the clinical transparency and interpretability of the model. Overall, this method advances deep learning for medical image analysis and supports precise diagnosis and treatment of renal tumors.