Article

Proof-of-principle neural network models for classification, attribution, creation, style-mixing, and morphing of image data for genetic conditions

2021-04-11

Abstract excerpt

Neural networks have shown strong potential to aid the practice of healthcare. Mainly due to the need for large datasets, these applications have focused on common medical conditions, where much more data is typically available. Leveraging publicly available data, we trained a neural network classifier on images of rare genetic conditions with skin findings. We used approximately100 images per condition to classif...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5a220271-0522-52fd-9fdc-37b17c93e89a
DOI: 10.1101/2021.04.08.21255123

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Proof-of-principle neural network models for classification, attribution, creation, style-mixing, and morphing of image data for genetic conditionsDOI 10.1101/2021.04.08.21255123Read

Neural networks for classification and image generation of aging in genetic syndromesSemantic proximity Read

GestaltGAN: Synthetic photorealistic portraits of individuals with rare genetic disordersSemantic proximity Read

Generative Adversarial Networks (GANs) for Medical Image Synthesis and Data AugmentationSemantic proximity Read

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal DiseaseSemantic proximity Read

High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited DataSemantic proximity Read

AECNet: Advancing Skin Cancer Classification using ACGAN-Based Synthetic Data with Grad-CAM ExplainabilitySemantic proximity Read

Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify ContoursSemantic proximity Read

Generative models improve fairness of medical classifiers under distribution shiftsSemantic proximity Read

Evaluation of a New Inclusive Next-Generation Synthetic Face Tool for Dysmorphology.Semantic proximity Read

Does Generative Adversarial Network Ensure Diversity in Data? Morphologic Evaluation of Synthetic Isocitrate Dehydrogenase-Mutant Glioblastomas in A Clinical Diagnostic ModelSemantic proximity Read

Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant typeSemantic proximity Read