Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally.

ABSTRACT: Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not only tedious and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method called AIFT (active, incremental fine-tuning) to naturally integrate active learning and transfer learning into a single framework. AIFT starts directly with a pre-trained CNN to seek "worthy" samples from the unannotated for annotation, and the (fine-tuned) CNN is further fine-tuned continuously by incorporating newly annotated samples in each iter

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC6191179 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature