IJCAI2020 & IJCV2021 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

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

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

PyTorch implementation of the MRNet paper, developed for the MRNet Competition hosted by the Stanford ML Group

My implementation for the MRNet competition hosted by the Stanford ML group

All credit goes to the authors listed at https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002699

CCNA Tutorial

😷 MRNet: A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset

Open|SpeedShop is a community effort by The Krell Institute with current direct funding from DOE’s NNSA and Office of Science. It is building on top of a broad list of community infrastructures, most notably Dyninst and MRNet from UW, libmonitor from Rice, and PAPI from UTK. Open|SpeedShop is an open source multi platform Linux performance tool which is targeted to support performance analysis of applications running on both single node and large scale Intel, AMD, ARM, Intel Phi, PPC, GPU processor based systems and on Blue Gene and Cray platforms.

Deep Learning CNN using FastAI for the Stanford MRNet Knee MRI diagnosis challenge

🛠 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 "Multi-scale Abstract Reasoning" paper

[CMIG 2022 / MIDL 2021] 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).

Mr. Network is a python reaction-network for molecular systems

No description available

Knee Lesions Classification via Deep Learning Techniques (MRNet model)

The Multicast/Reduction Network library.

No description available

This repo is free and it's not associated with official Kodi devs. These are 3rd party repositories and modules for 3rd party resources

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

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

A brand new CNN architecture for rock classification task detected by Chang'e 5 probe NaTeCam

The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports. this is an implementation of a CNN model to train the before mentioned data and see how accurately it predicted that the photo of the particular knee ( given 3 different views of it ) had any of the 3 labeled problems (Abnormality - ACL Tear - Meniscus Tear).

MRNet base repository

Implementation of the MRNet model for knee injury diagnosis

No description available

MRNet

Code for external validation experiment in "Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet"

Deep-learning-assisted diagnosis for knee magnetic resonance imaging

No description available