Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Harshita Sharma, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble. Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound. In 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France, April 13-16, 2021. pages 1646-1649, IEEE, 2021. [doi]

The following publications are possibly variants of this publication:

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videosHarshita Sharma, Lior Drukker, Pierre Chatelain, Richard Droste, Aris T. Papageorghiou, J. Alison Noble. mia, 69:101973, 2021. [doi]

Multimodal Continual Learning with Sonographer Eye-Tracking in Fetal UltrasoundArijit Patra, Yifan Cai, Pierre Chatelain, Harshita Sharma, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble. miccai 2021: 14-24 [doi]

Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasoundHe Zhao 0002, Qingqing Zheng, Clare Teng, Robail Yasrab, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble. mia, 90:102977, December 2023. [doi]

Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion TrackingYipei Wang, Richard Droste, Jianbo Jiao, Harshita Sharma, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble. miccai 2020: 180-188 [doi]

runs on WebDSL