معرفی شرکت ها
HEMnet-1.0.0
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مشاهده بیشترتوضیحات
HEMnet package
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | HEMnet-1.0.0 |
| نام | HEMnet |
| نسخه کتابخانه | 1.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Andrew Su, Xiao Tan and Quan Nguyen |
| ایمیل نویسنده | a.su@uqconnect.edu.au, xiao.tan@uq.edu.au, quan.nguyen@uq.edu.au |
| آدرس صفحه اصلی | https://github.com/BiomedicalMachineLearning/HEMnet |
| آدرس اینترنتی | https://pypi.org/project/HEMnet/ |
| مجوز | - |
[](https://mybinder.org/v2/gh/BiomedicalMachineLearning/HEMnet/master?filepath=Development)
[](https://imjoy.io/#/app?plugin=https://github.com/BiomedicalMachineLearning/HEMnet/blob/master/Demo/HEMnet_Tile_Predictor.imjoy.html)
[](https://colab.research.google.com/github/BiomedicalMachineLearning/HEMnet/blob/master/Demo/TCGA_Inference.ipynb)
# HEMnet - Haematoxylin & Eosin and Molecular neural network
## Description
A deep learning automated cancer diagnosis software using molecular labelling to improve pathological annotation of
Haematoxylin and Eosin (H&E) stained tissue.
## Installation
1. Docker
You can download and run the docker image using the following commands:
```
docker pull andrewsu1/hemnet
docker run -it andrewsu1/hemnet
```
2. Conda
Install Openslide (this is necessary to open whole slide images) - download it [here](https://openslide.org/download/)
Create a conda environment from the `environment.yml` file
```
conda env create -f environment.yml
conda activate HEMnet
```
## Usage
### Slide Preparation
Name slides in the format: `slide_id_TP53` for TP53 slides and `slide_id_HandE` for H&E slides
The `TP53` and `HandE` suffix is used by HEMnet to identify the stain used.
### 1. Generate training and testing datasets
a. Generate train dataset
`python HEMnet_train_dataset.py -b /path/to/base/directory -s relative/path/to/slides -o relative/path/to/output/directory
-t relative/path/to/template_slide.svs -v`
b. Generate test dataset
`python HEMnet_test_dataset.py -b /path/to/base/directory -s /relative/path/to/slides -o /relative/path/to/output/directory
-t relative/path/to/template_slide -m tile_mag -a align_mag -c cancer_thresh -n non_cancer_thresh`
Other parameters:
* `-t` is the relative path to the template slide from which all other slides will be normalised against. The template
slide should be the same for each step.
* `-m` is the tile magnification. e.g. if the input is `10` then the tiles will be output at 10x
* `-a` is the align magnification. Paired TP53 and H&E slides will be registered at this magnification.
To reduce computation time we recommend this be less than the tile magnification - a five times downscale generally works well.
* `-c` cancer threshold to apply to the DAB channel. DAB intensities less than this threshold indicate cancer.
* `-n` non-cancer threshold to apply to the DAB channel. DAB intensities greater than this threshold indicate no cancer.
### 2. Train and evaluate model
a. Training model
`python train.py -b /path/to/base/directory -t relative/path/to/training_tile_directory -l relative/path/to/validation_tile_directory
-o /relative/path/to/output/directory -m cnn_base -g num_gpus -e epochs -a batch_size -s -w -f -v`
Other parameters:
* `-m` is CNN base model. eg. `resnet50`, `vgg16`, `vgg19`, `inception_v3` and `xception`.
* `-g` is number of GPUs for training.
* `-e` is training epochs. Default is `100` epochs.
* `-a` is batch size. Default is `32`
* `-s` is option to save the trained model weights.
* `-w` is option to used transfer learning. Model will used pre-trained weights from ImageNet at the initial stage.
* `-f` is fine-tuning option. Model will re-train CNN base.
b. Test model prediction
`python test.py -b /path/to/base/directory -t relative/path/to/test_tile_directory -o /relative/path/to/output/directory
-w model_weights -m cnn_base -g num_gpus -v`
Other parameters:
* `-w` is path to trained model. eg. `trained_model.h5`.
* `-m` is CNN base model (same to training step).
* `-g` is number of GPUs for prediction.
c. Evaluate model performance and visualise model prediction
`python visualisation.py -b /path/to/base/directory -t /relative/path/to/training_output_directory -p /relative/path/to/test_output_directory
-o /relative/path/to/output/directory -i sample`
Other parameters:
* `-t` is path to training outputs.
* `-p` is path to test outputs.
* `-i` is name of Whole Slide Image for visualisation.
### 3. Apply model to diagnose new images
`python HEMnet_inference.py -s '/path/to/new/HE/Slides/' -o '/path/to/output/directory/' -t '/path/to/template/slide/' -nn '/path/to/trained/model/' -v`
Predict on TCGA images with our pretrained model for colorectal cancer using [google colab](https://colab.research.google.com/github/BiomedicalMachineLearning/HEMnet/blob/master/Demo/TCGA_Inference.ipynb)
## Results
## Citing HEMnet
## The Team
Please contact Dr Quan Nguyen (quan.nguyen@uq.edu.au), Andrew Su (a.su@uqconnect.edu.au),
and Xiao Tan (xiao.tan@uqconnect.edu.au) for issues, suggestions,
and we are very welcome to collaboration opportunities.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl HEMnet-1.0.0:
pip install HEMnet-1.0.0.whl
نصب پکیج tar.gz HEMnet-1.0.0:
pip install HEMnet-1.0.0.tar.gz