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Found 5 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.
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.
Random Hinge Forest for Differentiable Learning
We propose random hinge forests, a simple, efficient, and novel variant of decision forests.
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
Face Detection with a 3D Model
This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters.
