Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images

Based on comprehensive benchmarking experiments, we have found distinct robustness properties of deep neural networks in semantic segmentation of FM images. Based on the findings, we have made specific recommendations on selection and design of robust models for FM image segmentation.

Authors

Lingrui Li (ORCID: https://orcid.org/0000-0001-7336-3396)
Ge Yang (ORCID: https://orcid.org/0000-0001-6176-3130)
Liqun Zhong

Institutions

Shandong Institute of Automation (CN)
Beijing Academy of Artificial Intelligence (CN)
Institute of Automation (CN)
University of Chinese Academy of Sciences (CN)

Publication Details

Journal: BMC Bioinformatics
Published: 2024-08-20
DOI: https://doi.org/10.1186/s12859-024-05894-4
Citations: 3
Primary Topic: Adversarial Robustness in Machine Learning
Type: article
Field-Weighted Citation Impact: 1.04

Funders

National Natural Science Foundation of China
Chinese Academy of Sciences
Fundamental Research Funds for the Central Universities

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article

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images

Lingrui Li, Ge Yang, Liqun Zhong

Aug 20, 2024

3 citations

BMC Bioinformatics

Adversarial Robustness in Machine Learning

1.04

article

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images

Lingrui Li, Ge Yang, Liqun Zhong

August 20, 2024article en

3 citations

Abstract

Based on comprehensive benchmarking experiments, we have found distinct robustness properties of deep neural networks in semantic segmentation of FM images. Based on the findings, we have made specific recommendations on selection and design of robust models for FM image segmentation.

BMC BioinformaticsVol. 25(1)

Shandong Institute of Automation (CN), Beijing Academy of Artificial Intelligence (CN), Institute of Automation (CN), University of Chinese Academy of Sciences (CN)

National Natural Science Foundation of China, Chinese Academy of Sciences, Fundamental Research Funds for the Central Universities

Peace, Justice and strong institutions

Openalex Percentile: Top 19%

Adversarial Robustness in Machine Learning

1.04

DOI Open Access PDF OpenAlex

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Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images — Lingrui Li, Ge Yang, et al. · BMC Bioinformatics (2024) | TGRS Research Map | TGRS