Search Results for author:
Found 3 papers, 1 papers with code
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model.
Conceptual Design Generation Using Large Language Models
Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty.
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images
Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0. 88 and DSC scores of 0. 78, 0. 40, and 0. 46 for segmentation of the kidney, cysts, and tumors, respectively.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
