MRNET

Deep-learning-assisted diagnosis for knee magnetic resonance imaging https://stanfordmlgroup.github.io/projects/mrnet/

1. input to the pretrained NASNet has dimensions s × 224 × 224 x 3, where s is the number of images in the MRI series.
2. each 2-dimensional MRI image slice is passed through a feature extractor to obtain a s × 1056 × 7 × 7 tensor containing features for each slice.
3. A global average pooling layer is then applied to reduce these features to s × 1056.
4. global max pooling across slices to obtain a 1056-dimensional vector
5. Pass the Features to a Dense Layer to train the model
6. Repeat it for the 9 models ( 3 views: Axial, Sagittal, Coronal with each symptom of Abnormal, ACL, meniscal tears)
7. Get the average of each symptom with the 3 views

Notes:

• Augmentation is used
• we pass the images to the pretrained model as we load the data to make best use of RAM resources
• save the extracted features in .csv files in order to be able to use it later