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two-views-classifier

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Two Views breast cancer classifier

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# two-views-classifier
Two Views breast cancer classifier. Two view classifier for breast cancer. This is the inference code of the EfficientNet-based classifier to classify the two mammography views at once. It was trained in CBIS-DDSM dataset with original test split. It means that any pair of mammograms in test set can be used in this inference.

### Breaking News! Third Update [year 2026]

The full source code for our two-view breast cancer classifiers — an evolution of the work presented in our published papers — is now available as a new public repository: [https://github.com/dpetrini/multiple-view-src](https://github.com/dpetrini/multiple-view-src)

### Second Update [year 2025]

Now we have new paper that extends this work:

[Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification](https://arxiv.org/abs/2503.19945)

With a new repo (that contains code useful and related to this former work, please check):
https://github.com/dpetrini/multiple-view

### Update [year 2025]

Added some code related to dataset preparation: [https://github.com/dpetrini/prepare_datasets/](https://github.com/dpetrini/prepare_datasets/)

Also, check the library we use for training the models: [https://github.com/dpetrini/nova](https://github.com/dpetrini/nova)

## Instructions for inference with two views

python3 2views_clf_test.py -h

```
usage: 2views_clf_test.py [-h] -c CC -m MLO [-d MODEL] [-a AUG]

[Poli-USP] Two Views Breast Cancer inference

optional arguments:
  -h, --help               show this help message and exit
  -c CC, --cc CC           CC image file.
  -m MLO, --mlo MLO        MLO image file.
  -d MODEL, --model MODEL  two-views detector model (default model already included)
  -a AUG, --aug AUG        select to use translation augmentation: -a true
  
```

Example:
```
  python3 2views_clf_test.py -c samples/Calc-Test_P_00127_RIGHT_CC.png -m samples/Calc-Test_P_00127_RIGHT_MLO.png
```
Obs. Some sample files from CBIS-DDSM test set are included in samples folder for evaluation. Files were resized for network input.

Obs2. In order to perform test inference download our two-views model from [here](https://drive.google.com/file/d/1mOicNn1lCtXxXb2ficPmWFOnR4HM5c5M/view?usp=sharing) and place it in "models_side_mid_clf_efficientnet-b0" folder.

### Acknowlegments
Parts of EfficientNet from https://github.com/lukemelas/EfficientNet-PyTorch/ is included here and slightly modified, based in version 0.7.0.

### Dependencies
argparse

numpy

torch

cv2

### Reference
If you use want to know more, please check complete text [here](https://ieeexplore.ieee.org/document/9837037). If you want to cite this work please use reference below.

```
@ARTICLE{
9837037,
  author={Petrini, Daniel G. P. and Shimizu, Carlos and Roela, Rosimeire A. and Valente, Gabriel Vansuita and Folgueira, Maria Aparecida Azevedo Koike and Kim, Hae Yong},
  journal={IEEE Access}, 
  title={Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network}, 
  year={2022},
  volume={10},
  number={},
  pages={77723-77731},
  keywords={Mammography;Convolutional neural networks;Training;Transfer learning;Breast cancer;Artificial intelligence;Lesions;Breast cancer diagnosis;deep learning;convolutional neural network;mammogram;transfer learning},
  doi={10.1109/ACCESS.2022.3193250}
}

```

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