https://github.com/hilab-git/ife_ai
https://github.com/hilab-git/ife_ai
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/hilab-git/ife_ai
- Owner: HiLab-git
- Created: 2019-04-17T01:42:44.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-02-09T05:44:14.000Z (about 3 years ago)
- Last Synced: 2025-02-14T22:34:02.518Z (about 1 year ago)
- Language: Python
- Size: 216 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Immunofixation Electrophoresis (IFE) Image Recognition based on Deep Learning
This repository provides source code for IFE image recognition in the following paper:
* Honghua Hu, Wei Xu et al. "Expert-level Immunofixation Electrophoresis (IFE) Image Recognition based on Explainable and Generalizable Deep Learning". Clinical Chemistry, 69, no. 2 (2023): 130-139.
BibTeX entry:
@article{Hu2023IFE,
author = {Honghua Hu and Wei Xu and Ting Jiang and Yuheng Cheng and Xiaoyan Tao and Wenna Liu and Meiling Jian and Kang Li and Guotai Wang},
title = {Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning},
year = {2023},
url = {https://doi.org/10.1093/clinchem/hvac190},
journal = {Clinical Chemistry},
volume = {69},
issue = {2},
pages = {130-139},
}
# Requirements
* [Pytorch][torch_link] version >=1.9.0
* [PyMIC][pymic_link], a Pytorch-based toolkit for medical image computing. Version 0.2.5 is required. Install it by `pip install PYMIC==0.2.5`.
* Som basic python packages such as Numpy, Pandas, scipy.
* See `requirements.txt` for more details
# Image and preprocess
The images in this study are from two different systems that have different image styles (see `data/data_a` and `data/data_b`, respectively). We preprocess the images to make them have the same arrangement and size. The following figures show images before and after preprocessing.
To play with a demo for image preprocessing, run the following command:
```
python preprocess.py
```
# Demo for inference
To use the pretrained model for inference, download the checkpoints from [Google Drive][model_link] and save them to `ckpts`. Note that in the oringal paper, each of the three networks (VGG16, ResNet18 and MobileNetv2) has five checkpoints based on 5-fold cross validation. Due to the Google Drive space limit, we only upload one checkpoint for each network for model ensemble.
Run the following script for inference:
```
python demo_inference.py
```
By defualt, it uses the image `data/data_a/20200824_1012358442.jpg` from group `a` as an example. You can set different image names, such as an image from group `b` by editing line 72:
```
img_name, group ="data/data_b/9971568DTouch64.jpg", "b"
```
[torch_link]:https://pytorch.org
[pymic_link]:https://github.com/HiLab-git/PyMIC
[model_link]:https://drive.google.com/drive/folders/1w6e5omz_CdiOyGSViUOtWedM9kXGVr7J?usp=sharing