ArticleSensors (Basel, Switzerland)2019
HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Merging synthetic and real embryo data for advanced AI predictions.Scientific reports · 2025Article
- Combined Input Deep Learning Pipeline for Embryo Selection for In Vitro Fertilization Using Light Microscopic Images and Additional Features.Journal of imaging · 2025Article
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- A brief history of artificial intelligence embryo selection: from black-box to glass-box.Human reproduction (Oxford, England) · 2024Review
- Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.Bioinformatics (Oxford, England) · 2023Article
- Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention.Sensors (Basel, Switzerland) · 2021Article
- Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.Diagnostics (Basel, Switzerland) · 2021Article
- Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging.Frontiers in psychology · 2021Review
- Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer.Computational and structural biotechnology journal · 2021Article
- Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments.Sensors (Basel, Switzerland) · 2020Article
- Generative Adversarial Networks and Its Applications in Biomedical Informatics.Frontiers in public health · 2020Review
- Towards the automation of early-stage human embryo development detection.Biomedical engineering online · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose a method for generating the synthetic images of human embryo cells that could later be used for classification, analysis, and training, thus resulting in the creation of new synthetic image datasets for research areas lacking real-world data. Our focus was not only to generate the generic image of a cell such, but to make sure that it has all necessary attributes of a real cell image to provide a fully realistic synthetic version. We use human embryo images obtained during cell development processes for training a deep neural network (DNN). The proposed algorithm used generative adversarial network (GAN) to generate one-, two-, and four-cell stage images. We achieved a misclassification rate of 12.3% for the generated images, while the expert evaluation showed the true recognition rate (TRR) of 80.00% (for four-cell images), 86.8% (for two-cell images), and 96.2% (for one-cell images). Texture-based comparison using the Haralick features showed that there is no statistically (using the Student's t-test) significant (
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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.