🩺 Automate knee MRI segmentation using DiffuKnee’s diffusion model and U-Net pipeline for accurate and efficient multi-class results.
A PyTorch implementation of a diffusion-model + U-Net pipeline for automated multi-class segmentation of knee MRI scans. Includes dataset utilities, training/evaluation workflows, and 2D/3D inference scripts, with Docker and conda support for reproducibility.
Deep Learning Based Diagnosis, Abnormality and Common Disorders detection using Knee MRI Stanford MRNet Dataset
Knee bone and cartilage segmentation in 3D MRI
RSNA Knee MRI: multi-label abnormality detection via text-guided knowledge distillation. A 3D ViT student distills a DINO-3D teacher and a Gemma VLM into weak labels + visual guidance to predict 12 knee conditions from MR volumes.
Explainable deep learning on the MRNet knee MRI dataset (CAM + SHAP) with scripts for training, attribution, and visualization.
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