PathAL: An Active Learning Framework for Histopathology Image Analysis.

ABSTRACT: Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseud

SUBMITTER: Li W 

PROVIDER: S-EPMC9199991 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature