Combined Radiology and Pathology Classification

MICCAI 2020 Combined Radiology and Pathology Classification Challenge (1st place solution)

Hardware

4*NVIDIA Tesla P40 GPU cards
32GB of RAM

Pre-requisites:

torch >=1.3.0, nibabel, batchgenerators

Usage

Preparation

Data Preparation

Download challenge data

Training Splits (spilt/train1.txt)

MRI training

Trainning Glioblastoma/None Glioblastoma(pretrain 3d medical Weights )

cd mri
python train_g.py

RESNET=False  #False is resnet, True is densenet
model.conv1 = nn.Conv3d(4,....)  #The input channel is 5 if the tumor segmentation region exists, otherwise it is 4



model = densenet.densenet121(first=5，..)  #The input channel is 5 if the tumor segmentation region exists, otherwise it is 4 
#datasets.brain.py
BrainDataset_AO,BrainDataset_G # AO dataset，G_dataset  
return img_array[:4], labels #The input channel is 5 if the tumor segmentation region exists, otherwise it is 4

2.Trainning Oligodendroglioma/Lower grade astrocytoma
After the first stage of training, the second stage of training needs to use the weights trained in the first stage to warm up