Graham, S., Shaban, M., Qaiser, T. et al. (3 more authors) (2018) Classification of lung cancer histology images using patch-level summary statistics. In: Tomaszewski, J.E. and Gurcan, M.N., (eds.) SPIE Medical Imaging 2018: Digital Pathology. SPIE Medical Imaging 2018: Digital Pathology, 10-15 Feb 2018, Houston, Texas, USA. Society of Photo-optical Instrumentation Engineers (SPIE) ISBN 9781510616516
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Non-small cell lung cancer; histology image classification; computational pathology; deep learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Sep 2019 14:49 |
Last Modified: | 04 Sep 2019 14:49 |
Status: | Published |
Publisher: | Society of Photo-optical Instrumentation Engineers (SPIE) |
Refereed: | Yes |
Identification Number: | 10.1117/12.2293855 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150422 |