Title: AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts,
Abstract: The splendid success of convolutional neural networks (CNNs) in computer
vision is largely attributed to the availability of large annotated datasets,
such as ImageNet and Places. However, in biomedical imaging, it is very
challenging to create such large annotated datasets, as annotating biomedical
images is not only tedious, laborious, and time consuming, but also demanding
of costly, specialty-oriented skills, which are not easily accessible. To
dramatically reduce annotation cost, this paper presents a novel method to
naturally integrate active learning and transfer learning (fine-tuning) into a
single framework, called AFT*, which starts directly with a pre-trained CNN to
seek "worthy" samples for annotation and gradually enhance the (fine-tuned) CNN
via continuous fine-tuning. We have evaluated our method in three distinct
biomedical imaging applications, demonstrating that it can cut the annotation
cost by at least half, in comparison with the state-of-the-art method. This
performance is attributed to the several advantages derived from the advanced
active, continuous learning capability of our method. Although AFT* was
initially conceived in the context of computer-aided diagnosis in biomedical
imaging, it is generic and applicable to many tasks in computer vision and
image analysis; we illustrate the key ideas behind AFT* with the Places
database for scene interpretation in natural images.