DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical images is knowledge-driven, time-consuming, and labor-intensive, makin...

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Bibliographic Details
Main Authors:	Chen, C. (Author), Guan, C. (Author), Xu, K. (Author), Zeng, Z. (Author), Zhao, Z. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	1,2-dehydrosalsolinol Active learning Active learning methods Active learning strategies article attention Biomedical image segmentation Biomedical images Computational costs deep learning Deep learning ensemble learning Ensemble learning human Human annotations human experiment Humans image processing Image Processing, Computer-Assisted image segmentation Image segmentation Internet internet of medical things isoquinoline derivative Isoquinolines Iterative methods Learning systems Medical imaging Medical problems problem based learning Problem-Based Learning semi-supervised learning Semi-supervised learning supervised machine learning Supervised Machine Learning Supervision mechanisms uncertainty
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DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

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