Pituitary Microadenoma Diagnosis using Deep Learning
This repository contains the source code of PM-CAD system which is described in our paper "Automatic diagnosis of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms". The PM-CAD system is a computer-aided diagnosis system aiming to diagnose pituitary microadenoma from MRI using deep learning techniques.
Dependencies
- pytorch >= 1.6.0
- SimpleITK >= 1.1.0
- albumentations >= 0.4.3
- tensorflow >= 2.0.0
- numpy >=1.16.3
- opencv-Python >= 4.1.2.30
- matplotlib >= 2.2.2
- tqdm >= 4.46.0
Preparation
Download the checkpoints from BaiduYunPan [Link] with code frnp
.
Demo
We provide a snippet code to demonstrate how to diagnose pituitary microadenomas using our method, run by:
python demo.py --path=Dataset/Demo/pituitary_microadenoma_case
Train
To train the pituitary detection model:
cd DetectionModel
sh experiment/fasterrcnn/train_FasterRCNN.sh
To train the microadenomas diagnosis model (i.e., PM-Net):
cd DiagnosisModel
sh experiment/panet/run_PANet.py
To train other microadenomas diagnosis models:
cd DiagnosisModel
sh experiment/baseline/train_VGG16.sh
sh experiment/baseline/train_ResNeXt50.sh
sh experiment/baseline/train_ResNet50.sh
sh experiment/baseline/train_GoogLeNet.sh
sh experiment/baseline/train_DenseNet169.sh
sh experiment/cnn3d/train_CNN3D.sh