Loading patent…
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsZongwei Zhou, Jae Shin, Jianming Liang
Estimating the term…
Described herein are systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements, in which the trained networks are then utilized in the context of medical imaging. The success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, it is tedious, laborious, and time consuming to create large annotated datasets, and demands costly, specialty-oriented skills. A novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework is presented to dramatically reduce annotation cost, starting with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. The described method was evaluated using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.
A system comprising:
A computer-implemented method for actively and continually fine-tuning a convolutional neural network, wherein the method comprises:
A non-transitory computer readable storage media having instructions stored thereupon that, when executed by system having at least a processor and a memory therein, the instructions cause the processor to actively and continually fine-tune a convolutional neural network by performing the following operations:
Dependencies are inferred from the claim text.