capstone

BrainStation Capstone Project - Detecting Cancer using CNNs

Author: Jared Lauer

Following is a description of the files contained in this project. I recommend starting from the EDA notebook before proceeding to the CNN1, VGG16, then ResNet50.

1. EDA.ipynb

   Background and Exploratory Data Analysis for this project, which includes some background information on breast cancer, the PCam dataset, and why histopathological lymph node samples are relevant in cancer diagnosis. Next, I look at some sample images from the data

2. CNN1.ipynb

   The first CNN for this project is based on the architecture proposed by one of the authors of the dataset, Geert Litjens. I make an effort to improve the performance of this model through hyperparameter optimization and changing the architecture by adding different layer types.

3. VGG16.ipynb

   The first transfer learning model for this project is VGG16 which has been pretrained on the ImageNet database. Because the model in CNN1 is loosely based on this model architecture, I decided to examine it as well. I add some dense layers to the output of this model and examine its performance.

   Note that for this notebook and for ResNet50, the saved models are too large to be tracked using git, but the notebooks can be run by loading the history and y_proba for the analysis section. I will try and provide the saved models through Google Drive.

4. ResNet50.ipynb

   The second transfer learning model for this project is ResNet50 which has been pretrained on the ImageNet database. I add some dense layers to the output of this model and examine its performance.

   Note that for this notebook and for VGG16, the saved models are too large to be tracked using git, but the notebooks can be run by loading the history and y_proba for the analysis section. I will try and provide the saved models through Google Drive.

5. pcamlib.py

   A python script containing helper functions which appear in all of the various Jupyter notebooks, mainly used for loading the PCam dataset, saving and loading model outputs, and making the various plots I use to analyze the models.

6. GitHub Commands

   A notebook containing the necessary commands to clone, pull, and push (if you have an auth token ;) ) data from my capstone repo on GitHub. I recommend cloning my repo into Google Drive using this notebook if you intend to use Google Colab with this project.

7. data folder

   A folder containing saved models and other data which can be loaded into a notebook to analyze the performance of a trained model without training it again, saving time and ensuring repeatablility of results.

   Subfolders:

   data/models: saved copies of trained models created using the keras function model.save() data/models/y_proba: contains .csv files of trained model prediction probabilities. Especially useful for the transfer learning models which are too large to track using git when saving using model.save() data/plots: saved plots which may include confusion matrices, training histories, ROC curves, etc.

8. requirements.txt

   By running:

   conda create -n pcam --file requirements.txt

   This file can be used to create the pcam environment I used when running these notebooks locally, which has the same versions of all the packages used in the Python3 Colab environment. Note that it may be necessary to run pip install tensorflow==2.5.0 after creating this environment to update tensorflow, as conda automatically uses the latest version of tensorflow in conda-forge which is 2.4.1 and I have not tested these notebooks in this version. In particular, you may encounter issues when using load_model() to load one of the saved models into these notebooks if you are on an older version of tensorflow.