TY - GEN
T1 - Offline Versus Online Triplet Mining Based on Extreme Distances of Histopathology Patches
AU - Sikaroudi, Milad
AU - Ghojogh, Benyamin
AU - Safarpoor, Amir
AU - Karray, Fakhri
AU - Crowley, Mark
AU - Tizhoosh, Hamid R.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Extreme distances
KW - Histopathology
KW - Offline mining
KW - Online mining
KW - Triplet mining
KW - Triplet network
UR - http://www.scopus.com/inward/record.url?scp=85098203824&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098203824&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64556-4_26
DO - 10.1007/978-3-030-64556-4_26
M3 - Conference contribution
AN - SCOPUS:85098203824
SN - 9783030645557
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 333
EP - 345
BT - Advances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
A2 - Bebis, George
A2 - Yin, Zhaozheng
A2 - Kim, Edward
A2 - Bender, Jan
A2 - Subr, Kartic
A2 - Kwon, Bum Chul
A2 - Zhao, Jian
A2 - Kalkofen, Denis
A2 - Baciu, George
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Symposium on Visual Computing, ISVC 2020
Y2 - 5 October 2020 through 7 October 2020
ER -