TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos

[1]  Toru Tamaki,et al.  ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition , 2022, MMAsia.

[2]  Pheng-Ann Heng,et al.  Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition , 2021, Medical Image Anal..

[3]  Thomas M. Ward,et al.  SAGES consensus recommendations on an annotation framework for surgical video , 2021, Surgical Endoscopy.

[4]  N. Padoy,et al.  Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures , 2021, International Journal of Computer Assisted Radiology and Surgery.

[5]  Sang Won Im,et al.  Chiral Surface and Geometry of Metal Nanocrystals , 2019, Advanced materials.

[6]  Hao Chen,et al.  Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis , 2019, Medical Image Anal..

[7]  Satoshi Kondo,et al.  CATARACTS: Challenge on automatic tool annotation for cataRACT surgery , 2019, Medical Image Anal..

[8]  Didier Mutter,et al.  Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos , 2018, International Journal of Computer Assisted Radiology and Surgery.

[9]  Russell H. Taylor,et al.  Surgical data science for next-generation interventions , 2017, Nature Biomedical Engineering.

[10]  Yi Yang,et al.  Random Erasing Data Augmentation , 2017, AAAI.

[11]  G. Quellec,et al.  Real-time analysis of cataract surgery videos using statistical models , 2016, Multimedia Tools and Applications.

[12]  Rüdiger Dillmann,et al.  LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition , 2015, International Journal of Computer Assisted Radiology and Surgery.

[13]  A. Knoll,et al.  Toward increased autonomy in the surgical OR: needs, requests, and expectations , 2013, Surgical Endoscopy.