Active, continual fine tuning of convolutional neural networks for reducing annotation efforts

Author:

Zhou Zongwei,Shin Jae Y.,Gurudu Suryakanth R.,Gotway Michael B.,Liang Jianming

Funder

Mayo Clinic

Arizona State University

National Institutes of Health

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference101 articles.

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3. Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al., 2021. Big self-supervised models advance medical image classification. arXiv preprint arXiv:2101.05224.

4. Margin based active learning;Balcan,2007

5. The power of ensembles for active learning in image classification;Beluch,2018

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