Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization-Reference-Cited by-同舟云学术

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

Published:2024-08-27 Issue:1 Volume:15 Page:
ISSN:2041-1723
Container-title:Nature Communications
language:en
Short-container-title:Nat Commun

Author:

Rotem Oded^ORCID,Schwartz Tamar,Maor Ron,Tauber Yishay,Shapiro Maya Tsarfati^ORCID,Meseguer Marcos^ORCID,Gilboa Daniella,Seidman Daniel S.,Zaritsky Assaf^ORCID

Funder

Rosetrees Trust

Israel Council for Higher Education via the Data Science Research Center, BGU Wellcome Leap - delta tissue

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41467-024-51136-9.pdf

Reference105 articles.

1. Gulshan, V. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 316, 2402–2410 (2016).

2. Ting, D. S. W. et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA 318, 2211–2223 (2017).

3. Hacisoftaoglu, R. E., Karakaya, M. & Sallam, A. B. Deep Learning Frameworks for Diabetic Retinopathy Detection with Smartphone-based Retinal Imaging Systems. Pattern Recognit. Lett. 135, 409–417 (2020).

4. Ruamviboonsuk, P. et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health 4, e235–e244 (2022).

5. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).