This repo contains code for the MRNet Challenge
For more details refer to https://stanfordmlgroup.github.io/competitions/mrnet/
put train and valid folder inside images folder at root directory. Also put all labels inside
images folder.
The dataset contains MRIs of different people. Each MRI consists of multiple images. Each MRI has data in 3 perpendicular planes. And each plane as variable number of slices.
Each slice is an 256x256 image
For example:
For MRI 1 we will have 3 planes:
Plane 1- with 35 slices
Plane 2- with 34 slices
Place 3 with 35 slices
Each MRI has to be classisifed against 3 diseases
Major challenge with while selectingt the model structure was the inconsistency in the data. Although the image size remains constant , the number of slices per plane are variable within a single MRI and varies across all MRIs.
So we are proposing a model for each plane. For each model the batch size will be variable and equal to number of slices in the plane of the MRI. So training each model, we will get features for each plane.
We also plan to have 3 separate models for each disease. --NEEDS TO BE DISCUSSED
We will be using Resnet50 pretrained as a feature extractor. When we would have trained the 3 models on the 3 planes, we will use its feature extractor layer as an input to a global model for the final classification
We might even use a fixed batch size model for each plane and treat our data set as randomly distributed images for a plane. Anyways we have to figure out how to come up with an structure that aloows us to extract maximun info from the MRIs.