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Proceedings Paper

Classification of lung cancer histology images using patch-level summary statistics
Author(s): Simon Graham; Muhammad Shaban; Talha Qaiser; Navid Alemi Koohbanani; Syed Ali Khurram; Nasir Rajpoot
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Paper 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.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058119 (6 March 2018); doi: 10.1117/12.2293855
Show Author Affiliations
Simon Graham, The Univ. of Warwick (United Kingdom)
Muhammad Shaban, The Univ. of Warwick (United Kingdom)
Talha Qaiser, The Univ. of Warwick (United Kingdom)
Navid Alemi Koohbanani, The Univ. of Warwick (United Kingdom)
Syed Ali Khurram, The Univ. of Sheffield (United Kingdom)
Nasir Rajpoot, The Univ. of Warwick (United Kingdom)

Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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