Paper Info
Title
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection.
Abstract
While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis.
Year
DOI
Venue
2017
10.15353/vsnl.v3i1.177
arXiv: Computer Vision and Pattern Recognition
Field
DocType
Volume
Early detection,Basal cell carcinoma,Computer science,Medical imaging,Skin cancer,Artificial intelligence,Computational biology,Melanoma,Cancer,Radiomics,Machine learning
Journal
abs/1709.08248
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
1
2
Name
Order
Citations
PageRank
M. J. Shafiee110022.85
Alexander Wong235169.61