# Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
This repository contains the source code for our paper, [YNet](https://arxiv.org/abs/1806.01313), which is accepted for publication at [MICCAI'18](https://www.miccai2018.org/en/).
## Sample output of Y-Net
Y-Net identified correctly classified tissues that were not important for diagnosis. For example, stroma was identified as an important tissue, but blood was not. Stroma is an important tissue label for diagnosing breast cancer [1] and removing information about stroma decreased the diagnostic classification accuracy by about 4\%. See paper for more details.
[1] Beck, Andrew H., et al. "Systematic analysis of breast cancer morphology uncovers stromal features associated with survival." Science translational medicine 3.108 (2011): 108ra113-108ra113.
![Results](/images/results.png)
Some segmentation results (Left: RGB WSI, Middle: Ground truth, Right: Predictions by Y-Net)
![Results](/images/results.gif)
## Structure of this repository
YNet is trained in two stages:
* [stage1](/stage1/) This directory contains the source code for training the stage 1 in Y-Net. Stage 1 is nothing but a segmentation brach.
* [stage2](/stage2/) This directory contains the source code for training the stage 2 in Y-Net. Stage 2 is jointly learning the segmentation and classification.
* [seg_eval](/seg_eval/) This directory contains the source code for producing the segmentation masks.
## Pre-requisite
To run this code, you need to have following libraries:
* [OpenCV](https://opencv.org/) - We tested our code with version 3.3.0. If you are using other versions, please change the source code accordingly.
* [PyTorch](http://pytorch.org/) - We tested with v0.2.0_4. If you are using other versions, please change the source code accordingly.
* Python - We tested our code with Python 3.6.2 (Anaconda custom 64-bit). If you are using other Python versions, please feel free to make necessary changes to the code.
We recommend to use [Anaconda](https://conda.io/docs/user-guide/install/linux.html). We have tested our code on Ubuntu 16.04.
## Citation
If Y-Net is useful for your research, then please cite our paper.
```
@inproceedings{mehta2018ynet,
title={{Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images}},
author={Sachin Mehta and Ezgi Mercan and Jamen Bartlett and Donald Weaver and Joann Elmore and Linda Shapiro},
booktitle={International Conference on Medical image computing and computer-assisted intervention},
year={2018},
organization={Springer}
}
@article{mehta2018espnet,
title={ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation},
author={Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh Hajishirzi},
journal={European Conference in Computer Vision (ECCV)},
year={2018}
}
```
## License
This code is released under the same license terms as [ESPNet](https://github.com/sacmehta/ESPNet).
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Y-net资料.rar (57个子文件)
Y-net资料
YNet-master
images
results.png 1.05MB
ReadMe.md 65B
results.gif 2.01MB
LICENSE 1KB
stage1
IOUEval.py 2KB
main.py 12KB
Model.py 14KB
data
val.txt 362B
ReadMe.md 650B
train.txt 382B
valannot
1222_1_1200_4800_4.png 5KB
1339_1_1000_9800_1.33.png 2KB
1529_1_6000_3000_1.33.png 2KB
ReadMe.md 68B
1222_1_7200_3600_2.png 5KB
2204_1_18400_11800_1.33.png 5KB
trainrgb
1222_1_1200_4800_4.png 380KB
1339_1_1000_9800_1.33.png 271KB
1529_1_6000_3000_1.33.png 334KB
ReadMe.md 46B
1222_1_7200_3600_2.png 359KB
2204_1_18400_11800_1.33.png 315KB
trainannot
1222_1_1200_4800_4.png 5KB
1339_1_1000_9800_1.33.png 2KB
1529_1_6000_3000_1.33.png 2KB
ReadMe.md 242B
1222_1_7200_3600_2.png 5KB
2204_1_18400_11800_1.33.png 5KB
valrgb
1222_1_1200_4800_4.png 380KB
1339_1_1000_9800_1.33.png 271KB
1529_1_6000_3000_1.33.png 334KB
ReadMe.md 62B
1222_1_7200_3600_2.png 359KB
2204_1_18400_11800_1.33.png 315KB
Transforms.py 4KB
ReadMe.md 1KB
DataSet.py 967B
loadData.py 5KB
Criteria.py 700B
VisualizeGraph.py 2KB
pretrained_models_st1
model_C1.pth 11.34MB
ReadMe.md 361B
stage2
IOUEval.py 2KB
main.py 15KB
Model.py 22KB
Transforms.py 4KB
ReadMe.md 1KB
DataSet.py 1KB
loadData.py 5KB
Criteria.py 700B
pretrained_model_st2
ynet_c1.pth 15MB
ReadMe.md 361B
VisualizeGraph.py 2KB
README.md 3KB
seg_eval
Eval_YNet.py 2KB
ReadMe.md 153B
mehta2018.pdf 1.6MB
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