Back
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
Journal article

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

  • Chen, Yijiang Case Western Reserve University, Cleveland, OH
  • Janowczyk, Andrew Precision Oncology Center, Lausanne University Hospital, Lausanne, Switzerland
  • Madabhushi, Anant Louis Stokes Cleveland Veterans Affair Medical Center, Cleveland, OH
Published in:
  • JCO Clinical Cancer Informatics. - American Society of Clinical Oncology (ASCO). - 2020, no. 4, p. 221-233
English PURPOSE 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. METHODS We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.
Language
  • English
Open access status
hybrid
Identifiers
Persistent URL
https://sonar.ch/global/documents/161567
Statistics
Document views: 6 File downloads: