TUNet: Incorporating segmentation maps to improve classification

2019/01/27 by Yijun Tian, Tian, Yijun
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1901.11379

openalex publication_date 2019/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases. Among imaging techniques, high-throughput fluorescence microscopy imaging is an efficient biotechnology to stain the protein of interest in a cell. In this work, we present a novel classification model Twin U-Net (TUNet) for processing and classifying the belonging of protein in the Atlas images. Several notable Deep Learning models including GoogleNet and Resnet have been employed for comparison. Results have shown that our system obtaining competitive performance.

Citations

• Deep Learning in Neural Networks: An Overview
• Densely Connected Convolutional Networks
• Deep Residual Learning for Image Recognition

Related

• Extracting Cellular Location of Human Proteins Using Deep Learning
• Deep localization of protein structures in fluorescence microscopy images
• Learning multi-scale functional representations of proteins from single-cell microscopy data
• Neural network-assisted single-molecule localization microscopy with a weak-affinity protein tag
• U-Net: Convolutional Networks for Biomedical Image Segmentation