USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

ArXi:2604.07141v1 Announce Type: new Kidney stone disease ranks among the most prevalent conditions in urology, and understanding the composition of these stones is essential for creating personalized treatment plans and preventing recurrence. Current methods for analyzing kidney stones depend on postoperative specimens, which prevents rapid classification before surgery. To overcome this limitation, we