Breast Cancer Detection in Low Resolution Images:
Machine learning systems exist for the automatic detection of breast cancer in histopathology whole-slide images with high confidence.Such systems can potentially automate large portions of conventional diagnostic procedures used to identify breast cancer, improving support fordiagnoses via digital second opinion or reducing cognitive load by shifting work away from medical personnel.
However, these current systems are complex as they often fully utilise high-resolution whole-slide images with dimensions that are hundreds ofthousands of pixels in width and height. Such images represent pathology slides at considerably high magnification. Due to the high resolution of theimages, these systems are typically resource intensive, requiring either significant time or compute power, which hinders their clinical viability.
This project investigates automated breast cancer detection via deep learning techniques using lower resolution images (i.e., digital histopathologyslides at a lower magnification). The investigation intends to reveal whether machine learning models can be developed that provide high confidenceresults with some fractional amount of resources by using low- versus high-resolution whole-slide images.
Information On The Contents Of The Project Directory:
CS5199_Report.pdf
- The final report for the project in PDF format.
src/
- Contains the project source code. Primarily, this includes the model training and inference scripts. Also includedis thetools/ subdirectory containing the various data preparation scripts described in the report for making theinput data set suitable for use. This directory also includes aray_results/ folder, where the hyper-parameteroptimisation results are stored (not provided given size), and atensorboard/ directory where GUI outputs for trainingare stored (visible via the TensorBoard tool which is a requirement for the program environment). Full userinstructions for the project source code are found within the appendices of the report.
models/
- OMITTED (files too large): Contains the models created for this project. The model names included their expect input resolution (e.g., 299 for299 x 299 pixels) as well as the model version used (a0 is model version 0, etc.).
data/
- OMITTED (files too large): Contains the low-resolution data sets used in the project for training models at 299 x 299 pixels. This primarilyincludes the full Camelyon data set used in the project. Within the data set folder(s) are the train/, eval/, andtest/ sub-directories required by the implementation. Note these can be used to replicate the project work carriedout for models using 299 x 299 pixel inputs, but are not suitable for all higher input resolutions. For higherresolutions, the Camelyon data set will have to be downloaded and the data preparation process described in the userinstructions of the report will have to be followed.
testdata/
- Contains a small sub-set of the Camelyon data set used for debugging the source code. Serves no utility purpose now.
env/
- Contains the Dockerfile used to create the Docker container (i.e., program environment) at the remote GPU machine.This is provided for completeness. Also contains the requirements.txt file which lists the pip packages that arerequired to execute the project code. The only unlisted package required is python3-openslide, whose installation isshown in the Dockerfile.