# Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1803.04907 · HTML: https://mlsanity.com/paper/1803.04907/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 19 of 19)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks](https://mlsanity.com/paper/2203.03844/) (2022)
- [ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease](https://mlsanity.com/paper/2101.10799/) (2021)
- [Wavelet Feature Maps Compression for Image-to-Image CNNs](https://mlsanity.com/paper/2205.12268/) (2022)
- [Quantization of Deep Neural Networks for Accurate Edge Computing](https://mlsanity.com/paper/2104.12046/) (2021)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation](https://mlsanity.com/paper/2102.00221/) (2021)
- [QReg: On Regularization Effects of Quantization](https://mlsanity.com/paper/2206.12372/) (2022)
- [Pyramid U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2104.02333/) (2021)
- [Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations](https://mlsanity.com/paper/2109.00095/) (2021)
- [Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2209.12440/) (2022)
- [Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform](https://mlsanity.com/paper/2310.05262/) (2023)
- [EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation](https://mlsanity.com/paper/2306.06152/) (2023)
- [Fighting over-fitting with quantization for learning deep neural networks on noisy labels](https://mlsanity.com/paper/2303.11803/) (2023)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data](https://mlsanity.com/paper/2104.12284/) (2021)
- [Pruning and Quantization Impact on Graph Neural Networks](https://mlsanity.com/paper/2510.22058/) (2025)

## Abstract (arXiv)

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.