/MedDetection

A detection framework for 3D medical images

Primary LanguagePython

Medical Image Detection

A detection framework for 3D medical images based on PyTorch refers to the modular design of MMDetection. The framework consists of one-stage and two-stage detectors. More methods, instructions, and experiment results will be updated.

Installation

Requirements

  • g++ 7.5
  • gcc 7.5
  • cuda > 10.1
  • torch > 1.6.0

MedVision

git clone https://github.com/TimothyZero/MedVision.git
cd MedVision
pip install .

Train

git clone https://github.com/JoeeYF/MedDetection.git
cd MedDetection
python train.py --config config_path
bash run.sh config_name 1 1 1

Supported Methods

Detectors

  • Faster R-CNN
  • Cascade R-CNN
  • RetinaNet
  • DeepLung
  • CenterNet
  • yolov4

Backbone

  • ResNet
  • ResNeXt
  • SENet
  • Res2Net

Neck

  • FPN
  • PAN
  • BiFPN

DenseHead

  • RetinaHead
  • RPNHead

RoIHead

  • BaseRoIHead
  • DoubleHead
  • CascadeHead

Other features

  • OHEM
  • 3D DCNv2
  • 3D Soft-NMS

Dataset

Luna

The annotation json files are in COCO format.

Detection/Luna2016
├── train_images_test
    ├── subset0
        ├── ....nii.gz
    ├── subset1
    ├── subset2
    ├── ...
    ├── subset9
├── infer_dataset_0.json
├── infer_dataset_1.json
├── infer_dataset_2.json
├── ...
├── infer_dataset_9.json
├── train_dataset_0.json
├── train_dataset_1.json
├── train_dataset_2.json
├── ...
└── train_dataset_9.json

TODO

  • more methods support
  • experiment results
  • dataset structure

References

MedVision

MMdetection

medicaldetectiontoolkit