Deep active learning for classifying cancer pathology reports

Background: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount o...

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Bibliographic Details
Main Authors:	Alawad, M. (Author), Coyle, L. (Author), De Angeli, K. (Author), Doherty, J. (Author), Durbin, E.B (Author), Gao, S. (Author), Penberthy, L. (Author), Schaefferkoetter, N. (Author), Stroup, A. (Author), Tourassi, G. (Author), Wu, X.-C (Author), Yoon, H.-J (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Active learning Active learning strategies Active-learning algorithm algorithm Algorithms article cancer model Cancer pathology reports Classification (of information) Classification tasks clinical article comparative effectiveness controlled study convolutional neural network Convolutional neural networks deep learning Deep learning Diseases genetics histology histopathology human human experiment human tissue Humans Iterative methods Large dataset learning algorithm Learning algorithms Learning systems machine learning Machine Learning neoplasm Neoplasms Neural Networks, Computer pathology Pathology problem based learning Problem-Based Learning Sampling strategies Text classification Text classification models Text processing Training machines uncertainty Uncertainty samplings
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Deep active learning for classifying cancer pathology reports

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