  A convolutional neural network (ConvNet) is usually trained and then tested
using images drawn from the same distribution. To generalize a ConvNet to
various tasks often requires a complete training dataset that consists of
images drawn from different tasks. In most scenarios, it is nearly impossible
to collect every possible representative dataset as a priori. The new data may
only become available after the ConvNet is deployed in clinical practice.
ConvNet, however, may generate artifacts on out-of-distribution testing
samples. In this study, we present Targeted Gradient Descent (TGD), a novel
fine-tuning method that can extend a pre-trained network to a new task without
revisiting data from the previous task while preserving the knowledge acquired
from previous training. To a further extent, the proposed method also enables
online learning of patient-specific data. The method is built on the idea of
reusing a pre-trained ConvNet's redundant kernels to learn new knowledge. We
compare the performance of TGD to several commonly used training approaches on
the task of Positron emission tomography (PET) image denoising. Results from
clinical images show that TGD generated results on par with
training-from-scratch while significantly reducing data preparation and network
training time. More importantly, it enables online learning on the testing
study to enhance the network's generalization capability in real-world
applications.
