Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks

International Journal of Computer Assisted Radiology and Surgery
Victor Andrew A AntonioShigehiko Kanaya

Abstract

Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text]. We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multip...Continue Reading

References

Nov 1, 2006·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·D Neil HayesMatthew Meyerson
Jul 17, 2013·Journal of Pathology Informatics·Christopher D Malon, Eric Cosatto
Jul 26, 2013·Journal of Digital Imaging·Kenneth ClarkFred Prior
Aug 21, 2013·Journal of the American Medical Informatics Association : JAMIA·Sonal KothariMay D Wang
Dec 10, 2013·Proceedings·Nandita NayakBahram Parvin
Mar 1, 2014·Medical Image Computing and Computer-assisted Intervention : MICCAI·Hang ChangBahram Parvin
Aug 1, 2014·Nature·UNKNOWN Cancer Genome Atlas Research Network
May 16, 2015·Artificial Intelligence in Medicine·John ArevaloFabio A González
Jul 25, 2015·IEEE Transactions on Medical Imaging·Jun XuAnant Madabhushi
Sep 1, 2015·Translational Research : the Journal of Laboratory and Clinical Medicine·Jeremy T-H ChangR Stephanie Huang
Oct 7, 2015·IEEE Transactions on Medical Imaging·Holger RothRonald Summers

❮ Previous
Next ❯

Citations

May 21, 2021·Molecular Oncology·Krzysztof PastuszakTomasz Stokowy

❮ Previous
Next ❯

Methods Mentioned

BETA
feature extraction
biopsy
RISA

Software Mentioned

TensorFlow

Related Concepts

Related Feeds

Cancer -Omics

A variety of different high-throughput technologies can be used to identify the complete catalog of changes that characterize the molecular profile of cohorts of tumor samples. Discover the latest insights gained from cancer 'omics' in this feed.

Related Papers

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Heng Li Yonggang Shi
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Lei GuoLu Liu
Computational and Mathematical Methods in Medicine
Er-Yang HuanBing-Lin Huang
© 2022 Meta ULC. All rights reserved