  With pervasive applications of medical imaging in health-care, biomedical
image segmentation plays a central role in quantitative analysis, clinical
diagno- sis, and medical intervention. Since manual anno- tation su ers limited
reproducibility, arduous e orts, and excessive time, automatic segmentation is
desired to process increasingly larger scale histopathological data. Recently,
deep neural networks (DNNs), par- ticularly fully convolutional networks
(FCNs), have been widely applied to biomedical image segmenta- tion, attaining
much improved performance. At the same time, quantization of DNNs has become an
ac- tive 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
litera- ture on quantization which primarily targets memory and computation
complexity reduction, we apply quan- tization as a method to reduce over tting
in FCNs for better accuracy. Speci cally, we focus on a state-of- the-art
segmentation framework, suggestive annotation [22], which judiciously extracts
representative annota- tion samples from the original training dataset, obtain-
ing an e ective small-sized balanced training dataset. We develop two new
quantization processes for this framework: (1) suggestive annotation with
quantiza- tion 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.
