ArticleNature biomedical engineering2021
Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.
Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning.Journal of biomedical physics & engineering · 2026Article
- Dual knowledge-guided data augmentation for robust clinical prediction models.Scientific reports · 2026Article
- Stability and reliability of artificial intelligence models in embryo selection for in vitro fertilization.Fertility and sterility · 2026Article
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review.Annals of biomedical engineering · 2026Review
- Automated, Deep Learning-Enabled Immunoassay Microfluidic Cartridge for Viral Pathogen Detection.Advanced materials technologies · 2025Article
- A data-efficient strategy for building high-performing medical foundation models.Nature biomedical engineering · 2025Article
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.Science advances · 2025Article
- Article
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- The prospect of artificial intelligence to personalize assisted reproductive technology.NPJ digital medicine · 2024Review
- Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.Communications medicine · 2023Article
- Automation in ART: Paving the Way for the Future of Infertility Treatment.Reproductive sciences (Thousand Oaks, Calif.) · 2023Review
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Proceedings of the first world conference on AI in fertility.Journal of assisted reproduction and genetics · 2023Article
- Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks.Journal of assisted reproduction and genetics · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- Shifting machine learning for healthcare from development to deployment and from models to data.Nature biomedical engineering · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 1 country.
Funding
Abstract
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network.
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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.