MRNet

Introduced in Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet2018

About this Dataset

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.

Source: MRNet

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

Dataset Variants

MRNet

Papers1

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

A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported.

Dataset Loaders

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dazcona/mrnet

Tasks

EDIT

Multi-Label Classification Data Augmentation

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Statistics

Papers

1

Tasks

128

Introduced

2018