DeepAI AI Chat
Log In Sign Up

Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

by   Xiaowei Xu, et al.

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 reduction of up to 6.4x on memory usage.


page 2

page 5

page 7


MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation

Radiologist is "doctor's doctor", biomedical image segmentation plays a ...

Quantization of Deep Neural Networks for Accurate EdgeComputing

Deep neural networks (DNNs) have demonstrated their great potential in r...

Image Complexity Guided Network Compression for Biomedical Image Segmentation

Compression is a standard procedure for making convolutional neural netw...

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

Deep convolutional neural networks (CNNs), especially fully convolutiona...

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

3D image segmentation plays an important role in biomedical image analys...

U-Net Fixed-Point Quantization for Medical Image Segmentation

Model quantization is leveraged to reduce the memory consumption and the...

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Data

The random walker method for image segmentation is a popular tool for se...