Quantization of Fully Convolutional Networks for Accurate Biomedical
Image Segmentation
Abstract
With pervasive applications of medical imaging in
health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and
medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time,
automatic segmentation is desired to process increasingly
larger scale histopathological data. Recently, deep neural
networks (DNNs), particularly fully convolutional networks (FCNs), have been widely applied to biomedical image
segmentation, attaining much improved performance. At
the same time, quantization of DNNs has become an active research topic, which aims to represent weights with
less memory (precision) to considerably reduce memory
and computation requirements of DNNs while maintaining
acceptable accuracy. In this paper, we apply quantization
techniques to FCNs for accurate biomedical image segmentation. Unlike existing literatures on quantization which
primarily targets memory and computation complexity reduction, we apply quantization as a method to reduce over-
fitting in FCNs for better accuracy. Specifically, we focus
on a state-of-the-art segmentation framework, suggestive
annotation [26], which judiciously extracts representative
annotation samples from the original training dataset, obtaining an effective small-sized balanced training dataset.
We develop two new quantization processes for this framework: (1) suggestive annotation with quantization for highly representative training samples, and (2) network training
with quantization for high accuracy. Extensive experiments
on the MICCAI Gland dataset show that both quantization
processes can improve the segmentation performance, and
our proposed method exceeds the current state-of-the-art
performance by up to 1%. In addition, our method has a
reduction of up to 6.4x on memory usage