Training Lung Cancer Classifiers with Ultra-high Resolution Whole Slide Histopathology Images.

Principal Investigator

Name
Sean Yu

Degrees
M.S.

Institution
aetherAI

Position Title
Data Scientist

Email
seanyu@aetherai.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-620

Initial CDAS Request Approval
Dec 13, 2019

Title
Training Lung Cancer Classifiers with Ultra-high Resolution Whole Slide Histopathology Images.

Summary
Analysis of digital whole slide images (WSIs) is difficult because of its extremely high spatial resolution, up to billions of pixels. Applying CNN to learn patterns on such high-resolution images is thus a challenging task. Most approaches require an inefficient pre-processing procedure that crop a WSI into tens of thousands of small patches (normally 256 × 256) and annotate them for each WSI beforehand. These patch-based methods have yielded some successful results. However, the ground truth for each image patch needs to be given, which is typically done by free-hand contouring on the whole slide images. This annotation process is extremely laborious. Furthermore, borders between different tissue classes are often difficult to identify, leading to inconsistent annotation between pathologists. Lastly, the high variability of tissue morphology makes it difficult to cover all possible examples during annotation and to sample representative patches during training.
To deal with these drawbacks, we utilized the CUDA Unified Memory (UM) mechanism and optimized the workflow for reading and training deep convolutional neural networks with ultra-high resolution images directly. The ultra-patch method has already gained prominent results [1] on both nasopharyngeal carcinoma (NPC) classification and colorectal cancer lymphoma metastasis classification. However, the generalization ability of the ultra-patch method and its performance on TMAs still remain unknown. By using NLST pathology dataset, we can further validate the generalization of the ultra-patch method.

[1] Training Deep Neural Networks Directly on Hundred-million-pixel Histopathology Images on a Large-scale GPU Cluster, https://sc19.supercomputing.org/proceedings/tech_poster/poster_files/rpost144s2-file3.pdf

Aims

Train a deep convolutional neural networks for classifying normal/abnormal on each slide/microarray region of lung cancer slides.
Validate the performance between traditional patch-method and our ultra-patch method.

Collaborators

Chao-Yuan Yeh (aetherAI, joeyeh@aetherai.com)
Chi-Chung Chen (aetherAI, chenchc@aetherai.com)
Wei-Hsiang Yu (aetherAI, seanyu@aetherai.com)