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.

Result

• The result on slide 075

• THe result on slide 091

• The result on slide 096

Final Deliverable

• Notebook 1 - Data Sampling

  • Jupyter Notebook
  • Colab Version

• Notebook 2 - Data Modeling & Evaluation

  • Jupyter Notebook
  • Colab Version

• Final Model

  • Shared in Google drive
  • Shared in Google storage

• Project Introduction & Video Demo

  • Pdf version
  • Youtube Video Demo