Classification of lung cancer histology images using patch-level summary statistics

Simon Graham; Muhammad Shaban; Talha Qaiser; Navid Alemi Koohbanani; Syed Ali Khurram; Nasir Rajpoot

doi:10.1117/12.2293855

6 March 2018 Classification of lung cancer histology images using patch-level summary statistics

Simon Graham, Muhammad Shaban, Talha Qaiser, Navid Alemi Koohbanani, Syed Ali Khurram, Nasir Rajpoot

Proceedings Volume 10581, Medical Imaging 2018: Digital Pathology; 1058119 (2018) https://doi.org/10.1117/12.2293855
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

There are two main types of lung cancer: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are grouped accordingly due to similarity in behaviour and response to treatment. The main types of NSCLC are lung adenocarcinoma (LUAD), which accounts for about 40% of all lung cancers and lung squamous cell carcinoma (LUSC), which accounts for about 25-30% of all lung cancers. Due to their differences, automated classification of these two main subtypes of NSCLC is a critical step in developing a computer aided diagnostic system. We present an automated method for NSCLC classification, that consists of a two-part approach. Firstly, we implement a deep learning framework to classify input patches as LUAD, LUSC or non-diagnostic (ND). Next, we extract a collection of statistical and morphological measurements from the labeled whole-slide image (WSI) and use a random forest regression model to classify each WSI as lung adenocarcinoma or lung squamous cell carcinoma. This task is part of the Computational Precision Medicine challenge at the MICCAI 2017 conference, where we achieved the greatest classification accuracy with a score of 0.81.

Citation Download Citation

Simon Graham, Muhammad Shaban, Talha Qaiser, Navid Alemi Koohbanani, Syed Ali Khurram, and Nasir Rajpoot "Classification of lung cancer histology images using patch-level summary statistics", Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058119 (6 March 2018); https://doi.org/10.1117/12.2293855

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available