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

ABSTRACT:

Background

Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools for image segmentation. However, their performance on natural images may collapse under certain image corruptions or adversarial attacks. This poses real risks to their deployment in real-world applications. Although the robustness of DNN models in segmenting natural images has been studied extensively, their robustness in segmenting FM images remains poorly understood RESULTS: To address this deficiency, we have developed an assay that benchmarks robustness of DNN segmentation models using datasets of realistic synthetic 2D FM images wit

SUBMITTER: Zhong L 

PROVIDER: S-EPMC11334404 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature