ArticleScientific data2024
A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research.Materials today. Bio · 2025Article
- AutoQC-Bench: a diffusion model and benchmark for automatic quality control in high-throughput microscopy.Npj imaging · 2025Article
- PyPlaque is an open-source python package for phenotypic analysis of virus plaque assays.Scientific reports · 2025Article
- Artifact detection in fluorescence microscopy using convolutional autoencoder.Scientific reports · 2025Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
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2 authors.
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Abstract
High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.
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