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MI2RLNet

This MI2RLNet is the hub of pretrained models in the medical domain.

We hope MI2RLNet helps your downstream task.

  • Organizing Team : MI2RL, Asan Medical Center(AMC), Seoul, Republic of Korea

  • Contributor

    • Commiter : Kyuri Kim, Jiyeon Seo, Jooyoung Park, Mingyu Kim, Kyungjin Cho, Daeun Kim, Yujin Nam.

    • Reviewer : Sungman Cho, Sungchul Kim.

    • Data Maintainer : Miso Jang, Namkug Kim.


  • Docker images

    • Dockerfille : tensorflow > 2.x, Pytorch 1.x


Contents

Data description

Modality Part Module Data Reference
X-ray Chest L/R Mark Detection AMC
X-ray Chest PA / Lateral /Others Classification AMC
CT Chest Enhanced / Non-Enhanced Classification AMC
CT Chest Lung Segmentation AMC
CT Abdomen Kidnet & Tmuor Segmentation KiTS 2019
CT Abdomen Liver Segmentation AMC, LiTS 2017
Endoscopy Abdomen Polyp Detection Kvsair-SEG
MR Brain Brain Extraction AMC
MR Brain Blackblood Segmentation AMC

Experiment results

Modality Part Module Results Wiki Weights Framework
X-ray Chest L/R Mark Detection 0.99 (mAP) link link TF 2.x
X-ray Chest PA / Lateral / Others Classification 0.94 (Acc, external) link link TF 2.x
CT Chest Enhanced / Non-Enhanced Classification 0.96 (Acc, external) link link TF 2.x
CT Chest Lung Segmentation 0.98 (DSC) - link TF 2.x
CT Abdomen Kidney & Tumor Segmentation 0.83 (DSC) link link TF 2.x
CT Abdomen Liver Segmentation 0.97 (DSC) link link TF 2.x
Endoscopy Abdomen Polyp Detection 0.70 (DSC) link link Pytorch
MR Brain MRI/MRA BET (Brain Extration Tool) 0.95 (DSC) link MRI MRA Pytorch
MR Brain Blackblood Segmentation 0.83 (DSC) link link TF 2.x

How can we use ?

  • The example code below applies to almost all modules. Some modules may require additional parameters.

Inference

from medimodule.Abdomen import LiverSegmentation

# Initialize the model.
# If pre-trained weight exists, enter it together when the model is assigned.
model = LiverSegmentation("/path/of/weight")

# Get a result.
# If you want to save the result, enter it with `save_path` kwargs.
image, mask = model.predict("/path/of/image", save_path="/path/for/save")

Transfer Learning

# Import any module you want to fine-tune.
from medimodule.Abdomen import LiverSegmentation

# Initialize the model with pre-trained weight.
model = LiverSegmentation("/path/of/weight")

# Construct your custom training code.
...
model.train()
...

Contributing

If you'd like to contribute, or have any suggestions for these guidelines, you can contact us at [email protected] or open an issue on this GitHub repository.

All contributions welcome! All content in this repository is licensed under the Apache 2.0 license.