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https://github.com/codait/deep-histopath
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
https://github.com/codait/deep-histopath
cancer-research deep-learning machine-learning medical-imaging medicine
Last synced: about 1 month ago
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A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
- Host: GitHub
- URL: https://github.com/codait/deep-histopath
- Owner: CODAIT
- License: apache-2.0
- Created: 2017-09-08T22:38:49.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-03-07T23:27:28.000Z (almost 6 years ago)
- Last Synced: 2024-12-27T08:08:01.306Z (about 1 month ago)
- Topics: cancer-research, deep-learning, machine-learning, medical-imaging, medicine
- Language: Jupyter Notebook
- Homepage:
- Size: 49.5 MB
- Stars: 207
- Watchers: 22
- Forks: 88
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Predicting Breast Cancer Proliferation Scores with TensorFlow, Keras, and Apache Spark
Note: This project is still a **work in progress**. There is also an [experimental branch](https://github.com/dusenberrymw/systemml/tree/breast_cancer_experimental2/projects/breast_cancer) with additional files and experiments.
## Overview
The [Tumor Proliferation Assessment Challenge 2016 (TUPAC16)](http://tupac.tue-image.nl/) is a "Grand Challenge" that was created for the [2016 Medical Image Computing and Computer Assisted Intervention (MICCAI 2016)](http://miccai2016.org/en/) conference. In this challenge, the goal is to develop state-of-the-art algorithms for automatic prediction of tumor proliferation scores from whole-slide histopathology images of breast tumors.
## Background
Breast cancer is the leading cause of cancerous death in women in less-developed countries, and is the second leading cause of cancerous deaths in developed countries, accounting for 29% of all cancers in women within the U.S. [1]. Survival rates increase as early detection increases, giving incentive for pathologists and the medical world at large to develop improved methods for even earlier detection [2]. There are many forms of breast cancer including Ductal Carcinoma in Situ (DCIS), Invasive Ductal Carcinoma (IDC), Tubular Carcinoma of the Breast, Medullary Carcinoma of the Breast, Invasive Lobular Carcinoma, Inflammatory Breast Cancer and several others [3]. Within all of these forms of breast cancer, the rate in which breast cancer cells grow (proliferation), is a strong indicator of a patient’s prognosis. Although there are many means of determining the presence of breast cancer, tumor proliferation speed has been proven to help pathologists determine the best treatment for the patient. The most common technique for determining the proliferation speed is through mitotic count (mitotic index) estimates, in which a pathologist counts the dividing cell nuclei in hematoxylin and eosin (H&E) stained slide preparations to determine the number of mitotic bodies. Given this, the pathologist produces a proliferation score of either 1, 2, or 3, ranging from better to worse prognosis [4]. Unfortunately, this approach is known to have reproducibility problems due to the variability in counting, as well as the difficulty in distinguishing between different grades.
References:
[1] http://emedicine.medscape.com/article/1947145-overview#a3
[2] http://emedicine.medscape.com/article/1947145-overview#a7
[3] http://emedicine.medscape.com/article/1954658-overview
[4] http://emedicine.medscape.com/article/1947145-workup#c12
## Goal & Approach
In an effort to automate the process of classification, this project aims to develop a large-scale deep learning approach for predicting tumor scores directly from the pixels of whole-slide histopathology images (WSI). Our proposed approach is based on a recent research paper from Stanford [1]. Starting with 500 extremely high-resolution tumor slide images [2] with accompanying score labels, we aim to make use of Apache Spark in a preprocessing step to cut and filter the images into smaller square samples, generating 4.7 million samples for a total of ~7TB of data [3]. We then utilize TensorFlow and Keras to train a deep convolutional neural network on these samples, making use of transfer learning by fine-tuning a modified ResNet50 model [4]. Our model takes as input the pixel values of the individual samples, and is trained to predict the correct tumor score classification for each one. We also explore an alternative approach of first training a mitosis detection model [5] on an auxiliary mitosis dataset, and then applying it to the WSIs, based on an approach from Paeng et al. [6]. Ultimately, we aim to develop a model that is sufficiently stronger than existing approaches for the task of breast cancer tumor proliferation score classification.
References:
[1] https://web.stanford.edu/group/rubinlab/pubs/2243353.pdf
[2] http://tupac.tue-image.nl/node/3
[3] [`preprocess.py`](preprocess.py), [`breastcancer/preprocessing.py`](breastcancer/preprocessing.py)
[4] [`MachineLearning-Keras-ResNet50.ipynb`](MachineLearning-Keras-ResNet50.ipynb)
[5] [`preprocess_mitoses.py`](preprocess_mitoses.py), [`train_mitoses.py`](train_mitoses.py)
[6] https://arxiv.org/abs/1612.07180
---
## Setup (*All nodes* unless other specified):
* System Packages:
* `openslide`
* Python packages:
* Basics
* `pip3 install -U matplotlib numpy pandas scipy jupyter ipython scikit-learn scikit-image openslide-python`
* TensorFlow (only on driver):
* `pip3 install tensorflow-gpu` (or `pip3 install tensorflow` for CPU-only)
* Keras (bleeding-edge; only on driver):
* `pip3 install git+https://github.com/fchollet/keras.git`
* Spark 2.x (ideally bleeding-edge)
* Add the following to the `data` folder (same location on *all* nodes):
* `training_image_data` folder with the training slides.
* `testing_image_data` folder with the testing slides.
* `training_ground_truth.csv` file containing the tumor & molecular scores for each slide.
* `mitoses` folder with the following from the mitosis detection auxiliary dataset:
* `mitoses_test_image_data` folder with the folders of testing images
* `mitoses_train_image_data` folder with the folders of training images
* `mitoses_train_ground_truth` folder with the folders of training csv files
* Layout:
```
- MachineLearning-Keras-ResNet50.ipynb
- breastcancer/
- preprocessing.py
- visualization.py
- ...
- data/
- mitoses
- mitoses_test_image_data
- 01
- 01.tif
- 02
- 01.tif
...
- mitoses_train_ground_truth
- 01
- 01.csv
- 02.csv
...
- 02
- 01.csv
- 02.csv
...
...
- mitoses_train_image_data
- 01
- 01.tif
- 02.tif
...
- 02
- 01.tif
- 02.tif
...
...
- training_ground_truth.csv
- training_image_data
- TUPAC-TR-001.svs
- TUPAC-TR-002.svs
- ...
- testing_image_data
- TUPAC-TE-001.svs
- TUPAC-TE-002.svs
- ...
- preprocess.py
- preprocess_mitoses.py
- train_mitoses.py
```
* Adjust the Spark settings in `$SPARK_HOME/conf/spark-defaults.conf` using the following examples, depending on the job being executed:
* All jobs:
```
# Use most of the driver memory.
spark.driver.memory 70g
# Remove the max result size constraint.
spark.driver.maxResultSize 0
# Increase the message size.
spark.rpc.message.maxSize 128
# Extend the network timeout threshold.
spark.network.timeout 1000s
# Setup some extra Java options for performance.
spark.driver.extraJavaOptions -server -Xmn12G
spark.executor.extraJavaOptions -server -Xmn12G
# Setup local directories on separate disks for intermediate read/write performance, if running
# on Spark Standalone clusters.
spark.local.dirs /disk2/local,/disk3/local,/disk4/local,/disk5/local,/disk6/local,/disk7/local,/disk8/local,/disk9/local,/disk10/local,/disk11/local,/disk12/local
```
* Preprocessing:
```
# Save 1/2 executor memory for Python processes
spark.executor.memory 50g
```
* To execute the WSI preprocessing script, use `spark-submit` as follows (could also use Yarn in client mode with `--master yarn --deploy-mode client`):
```
PYSPARK_PYTHON=python3 spark-submit --master spark://MASTER_URL:7077 preprocess.py
```
* To execute the mitoses preprocessing script, use the following:
```
python3 preprocess_mitoses.py --help
```
* To execute the mitoses training script, use the following:
```
python3 training_mitoses.py --help
```
* To use the Jupyter notebooks, start up Jupyter like normal with `jupyter notebook` and run the desired notebook.
## Create a Histopath slide “lab” to view the slides (just driver):
- `git clone https://github.com/openslide/openslide-python.git`
- Host locally:
- `python3 path/to/openslide-python/examples/deepzoom/deepzoom_multiserver.py -Q 100 path/to/data/`
- Host on server:
- `python3 path/to/openslide-python/examples/deepzoom/deepzoom_multiserver.py -Q 100 -l HOSTING_URL_HERE path/to/data/`
- Open local browser to `HOSTING_URL_HERE:5000`.