alexa Characterization Of Tissue Histology Through Unsupervised Feature Learning
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Share This Page

Additional Info

Loading Please wait..

2nd International Summit on Integrative Biology
August 04-05, 2014 Hilton-Chicago/Northbrook, Chicago, USA

Hang Chang
Accepted Abstracts: J Comput Sci Syst Biol
DOI: 10.4172/0974-7230.S1.009
Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for tumor composition. Furthermore, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive models of the clinical outcome. However, performance of the existing techniques is hindered as a result of large technical variations and biological heterogeneities that are always present in a large cohort. On the other hand, in the machine learning community, deep learning techniques have recently emerged as strong candidates for applications, including decision support systems, voice recognition, and improved search engines. The unsupervised nature of deep learning techniques enables the automatic discovery of underlying complex patterns in the data, thus is desirable for large-scale scientific applications. The application of deep learning methods in the field of histopathology enables the leaning of high-level complex morphometric patterns preserved in the vast amount of WSIs, and as a result, leads to computational systems that are highly robust in the presence of large amount of technical variations and biological heterogeneities, and extensible to different tumor types.
Hang Chang has completed his PhD at the age of 27 years from Institution of Automation, Chinese Academy of Sciences. He is currently a career research scientist at the Life Sciences Division, Lawrence Berkeley National Laboratory. He has published more than 35 papers in reputed journals and conferences.
image PDF   |   image HTML

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version