Effective and efficient active learning for deep learning-based tissue image analysis.

ABSTRACT:

Motivation

Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface.

Results

We developed a framework that includes a friendly user interface along

SUBMITTER: Meirelles ALS 

PROVIDER: S-EPMC10079352 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature