ArticleeLife2020
On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Vestibular nucleus stimulation for ameliorating locomotor dynamics in a Parkinsonian mouse model.Nature communications · 2026Article
- Lipid droplet size profiling in yeast.Biology open · 2026Article
- TrkA abundance is increased in cutaneous nerves in bortezomib-induced neuropathy.Brain pathology (Zurich, Switzerland) · 2026Article
- Mirror Effect of Parvalbumin and Connexin 43 Expression in the Acute and Subacute Phases After Penetrating Traumatic Brain Injury Reveals a Non-Canonical Interaction.Molecules (Basel, Switzerland) · 2026Article
- Decreased parietal epithelial cell density is linked to podocyte depletion and predictors of kidney disease progression in human kidneys.American journal of physiology. Renal physiology · 2025Article
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- Human dorsal root ganglia are either preserved or completely lost after deafferentation by brachial plexus injury.British journal of anaesthesia · 2024Article
- Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin.Scientific reports · 2024Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.Cell structure and function · 2024Article
- Seeing or believing in hyperplexed spatial proteomics via antibodies: New and old biases for an image-based technology.Biological imaging · 2024Article
- DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.Development (Cambridge, England) · 2023Article
- Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.Scientific reports · 2023Article
- Deep learning-enabled segmentation of ambiguous bioimages with deepflash2.Nature communications · 2023Article
- Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy.Molecular biology of the cell · 2022Article
- Review
- SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy.PloS one · 2022Article
- Analysis of clonogenic growth in vitro.Nature protocols · 2021Review
- Bioimage Analysis and Cell Motility.Patterns (New York, N.Y.) · 2021Review
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.