Offline Versus Online Triplet Mining Based on Extreme Distances of Histopathology Patches

Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor, Fakhri Karray, Mark Crowley, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batch-wise), we also study the effect of extreme distances and neighbor patches before training in an offline fashion. We analyze extreme cases’ impacts in terms of embedding distance for offline versus online mining, including easy positive, batch semi-hard, batch hard triplet mining, neighborhood component analysis loss, its proxy version, and distance weighted sampling. We also investigate online approaches based on extreme distance and comprehensively compare offline, and online mining performance based on the data patterns and explain offline mining as a tractable generalization of the online mining with large mini-batch size. As well, we discuss the relations of different colorectal tissue types in terms of extreme distances. We found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study. Moreover, we found the assorted case, including different extreme distances, is promising, especially in the online approach.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
EditorsGeorge Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030645557
StatePublished - 2020
Event15th International Symposium on Visual Computing, ISVC 2020 - San Diego, United States
Duration: Oct 5 2020Oct 7 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12509 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Symposium on Visual Computing, ISVC 2020
Country/TerritoryUnited States
CitySan Diego


  • Extreme distances
  • Histopathology
  • Offline mining
  • Online mining
  • Triplet mining
  • Triplet network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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