Building an ACL tear , Meniscus tear and Knee abnormality detector to spot knee injuries from MRIs with PyTorch (MRNet)

IJCAI2020 & IJCV2021 🌇 Unsupervised Scene Adaptation with Memory Regularization in vivo

🛠 Licence plate detection and recognition tool. A new lightweight tool is able to recognize license plates in real time on devices with significant limitations in computing power.

Code for CVPR 2021 paper. "Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling".

Official implementation of the MRPyrNet architecture proposed in the papers "Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details" (MIDL 2021) and "Deep convolutional feature details for better knee disorder diagnoses in magnetic resonance images" (Computerized Medical Imaging and Graphics, 2022).

Deep Learning Based Diagnosis, Abnormality and Common Disorders detection using Knee MRI Stanford MRNet Dataset

Building an ACL tear detector to spot knee injuries from MRIs with PyTorch (MRNet)

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

Implementation of the MRNet model proposed in the paper "Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet" for knee disorders diagnosis.

A Knee MRI Dataset And Competition - Stanford Competition with MRI Dataset on knee injuries

This is an assignment for Pattern Recognition Course taught at Alexandria University, Faculty of Engineering offered in Spring 2019. Our Official Contribution on developing a Model for MRNet dataset.

Machine Learning project using python. detect the patient suffering from (abnormal OR ACL OR maniacal tears)from a different angles (Axial, Coronal, sagittal) and print the accuracy of this detect. Build a deep CNN model to perform the classification. Use Transfer Learning and ensemble. Plot accuracy, F-score, and loss of both training and valid…