For a more theoretical information please visit our conference paper at 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): https://ieeexplore.ieee.org/abstract/document/8759410

This repository contains the codes for reproducing the results obtained by out DeepHistoPathology model using an Ivasive Ductal Carcinoma open Dataset.

The deep learning models were implemented using Keras/Tensorflow framework.

The preprint for this work is available at https://arxiv.org/pdf/1901.03684.pdf

• Introduction
• Results
• Installation
• References
• How to cite DeepHP
• License

Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis and monitoring of the disease. However, identification of malignant cells can be challenging given the high heterogeneity in tissue absorbotion from staining agents. In this work, we present a novel approach for Invasive Ductal Carcinoma (IDC) cells discrimination in histopathology slides.

We used an open IDC dataset that can be found here:

http://andrewjanowczyk.com/wp-static/IDC_regular_ps50_idx5.zip

https://www.kaggle.com/paultimothymooney/breast-histopathology-images

The following figure shows some samples from the used Open IDC Dataset.
Left: three IDC cell patches; Right: three normal tissue patches.

We propose a model derived from the Inception architecture, proposing a multi-level batch normalization module between each convolutional steps. This module was used as a base block for the feature extraction in a CNN architecture. We used the open IDC dataset in which we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus surpassing recent state of the art classification algorithms tested on this public dataset

The following figure shows the used Inception modules that incorporates Batch normalization.

The following table shows the overall model architecture.

The following table presents the quantitative results of DeepHP Net compared on the same test set with other SOTA methods.

Qualitative results

The figure shows two HPS images. on the right the overlapped hearmap shows the IDC cell probability.

The firsts step is to clone this repository

git clone https://github.com/fperdigon/DeepHistoPathology

The next step is to create the Conda environment using the provided environment.yml file. For this step you need the conda python package manage installed. In case you don't have it installed we recommend installing Miniconda to avoid installing unnecessary Python packages.

To create the conda environment run the following command:

conda env create -f environment.yml

Then activate the Python environment you just created with the following command:

conda activate DeepHP

Finally start the training and the experiments by running the command:

python DeepHP_main.py

This python script will:

• Download the data to the data folder
• Extract the Data zip in the data folder
• Train the model
• Calculate performance metrics and display them in the console
• Generate confusion Matrix and ROC curve figures

Once the model is trained, for generating images with heatmaps run the following command:

python DeepHP_reconstructing_HPS_slides_main.py

If you have a Nvidia CUDA capable device for GPU acceleration this code will automatically use it (faster). Otherwise the training will be done in CPU (slower).

When citing DeepHP please use this BibTeX entry:

@INPROCEEDINGS{8759410,
author={Romero, Francisco Perdigоn and Tang, An and Kadoury, Samuel},
booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
title={Multi-Level Batch Normalization in Deep Networks for Invasive Ductal Carcinoma Cell Discrimination in Histopathology Images}, 
year={2019},
volume={},
number={},
pages={1092-1095},
doi={10.1109/ISBI.2019.8759410}}

The MIT License (MIT)

Copyright (c) 2021 Francisco Perdigon Romero

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.