Open AccessQuantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
Author(s) -
Yijiang Chen,
Andrew Janowczyk,
Anant Madabhushi
Publication year - 2020
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.19.00068
Subject(s) - computer science , telepathology , artificial intelligence , image compression , jpeg , segmentation , jpeg 2000 , data compression , compression ratio , digital pathology , pattern recognition (psychology) , computer vision , image processing , image (mathematics) , health care , telemedicine , internal combustion engine , automotive engineering , engineering , economics , economic growth
Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored.