Detecting Cancer Metastases on Gigapixel Pathology Images

Columbia University Applied Deep Learning Course Project (Fall 2019)
Author: Yingxiang Chen
Columbia Uni: yc3526
Video Demo: https://youtu.be/h6wJMuvgd4M

Objective

Current Situation:

Microscopic examination of lymph nodes is crucial in breast cancer staging
Currently, the manual process requires highly skilled pathologists
The process is fairly time-consuming and error-prone, particularly for lymph nodes with either no or small tumors

So I want to follow the strategy in the paper and utilize the deep learning models & techniques to relieve the workload of physicians by creating a workflow to detect and locate tumor pixels in the images and offer automatic second opinions.

Dataset

Raw Data: 21 Gigapixel Pathology Images, each has a tumor slide and a corresponding mask from The CAMELYON16 challenge.
Trainset: 8000 (4000 each at two different zoom levels) image patches sampled from 16 Gigapixel Pathology Images.
Validation set: 1600 (800 each at two different zoom levels) image patches sampled from 2 Gigapixel Pathology Images.
Test set: 3 Gigapixel Pathology Images.

Image augmentation

Adopt similar augmentation strategies discussed in the paper.

Use Keras ImageDataGenerator to augment data
- Horizontal_flip
- Vertical_flip
- Rescale
- Width_shift
- Height_shift
- Rotation
Use TensorFlow image random function to augment data
- Random brightness
- Random saturation
- Random hue
- Random contrast

Model

Transfer Learning: Used two pre-trained inception v3 models on Imagenet to speed up the training process.
Global Pooling: Applied GlobalAveragePooling layer after the inception model to significantly reduce parameters.