1st-in-MICCAI2020-CPM - Combined Radiology and Pathology Classification

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

1. Data Preparation

• Download challenge data

2. Training Splits (spilt/train1.txt)

MRI training

1. 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

cd mri
python train_ao.py

WSI training

Reference This is code based on MedicalNet

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