Cargando…

Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning

PURPOSE: The purpose of this study was to develop an unsupervised feature learning approach that automatically measures Meibomian gland (MG) atrophy severity from meibography images and discovers subtle relationships between meibography images according to visual similarity. METHODS: One of the late...

Descripción completa

Detalles Bibliográficos
Autores principales:	Yeh, Chun-Hsiao, Yu, Stella X., Lin, Meng C.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	The Association for Research in Vision and Ophthalmology 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873493/ https://www.ncbi.nlm.nih.gov/pubmed/34003889 http://dx.doi.org/10.1167/tvst.10.2.4

Ejemplares similares

Predicting demographics from meibography using deep learning
por: Wang, Jiayun, et al.
Publicado: (2022)

A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images
por: Wang, Jiayun, et al.
Publicado: (2019)

Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features
por: Li, Siyan, et al.
Publicado: (2023)

Noncontact Meibography in Patients with Keratoconus
por: Mohamed Mostafa, Engy, et al.
Publicado: (2019)

Meibography for eyes with posterior blepharitis
por: AlDarrab, Abdulrahman, et al.
Publicado: (2017)

Evaluation of ocular psoriasis with meibography()
por: Kemeriz, Funda, et al.
Publicado: (2022)

A Deep Learning Model for Evaluating Meibomian Glands Morphology from Meibography
por: Wang, Yuexin, et al.
Publicado: (2023)

A review of meibography for a refractive surgeon
por: Vunnava, Krishna Poojita, et al.
Publicado: (2020)

Quantitative analysis of morphological and functional features in Meibography for Meibomian Gland Dysfunction: Diagnosis and Grading
por: Deng, Yuqing, et al.
Publicado: (2021)

Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning
por: Yao, Kai, et al.
Publicado: (2019)

Differentiation between chalazion and sebaceous carcinoma by noninvasive meibography
por: Nemoto, Yuji, et al.
Publicado: (2014)

Risk Factors for Meibomian Gland Disease Assessed by Meibography
por: Kim, Christine K, et al.
Publicado: (2023)

Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography
por: Setu, Md Asif Khan, et al.
Publicado: (2021)

The Role of Meibography in the Diagnosis of Meibomian Gland Dysfunction in Ocular Surface Diseases
por: Robin, Mathieu, et al.
Publicado: (2019)

Proposed Algorithm for Management of Meibomian Gland Dysfunction Based on Noninvasive Meibography
por: Arita, Reiko, et al.
Publicado: (2020)

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
por: Stowell, Dan, et al.
Publicado: (2014)

Dissecting the Roles of Supervised and Unsupervised Learning in Perceptual Discrimination Judgments
por: Loewenstein, Yonatan, et al.
Publicado: (2021)

Unsupervised Phenotype-Based Clustering of Clinicopathologic Features in Cutaneous Melanoma
por: Rashid, Sarem, et al.
Publicado: (2021)

Penalized Model‐Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score
por: Sung, Kuo‐Tzu, et al.
Publicado: (2023)

Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification
por: Zhong, Junru, et al.
Publicado: (2023)

Non-contact meibography changes according to disease activity in rheumatoid arthritis cases
por: Mounir, Amr, et al.
Publicado: (2023)

Dynamic Graph Clustering Learning for Unsupervised Diabetic Retinopathy Classification
por: Yu, Chenglin, et al.
Publicado: (2023)

Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs
por: Loza, Carlos A., et al.
Publicado: (2019)

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data
por: Lasko, Thomas A., et al.
Publicado: (2013)

Clinical Trial of Thermal Pulsation (LipiFlow) in Meibomian Gland Dysfunction With Preteatment Meibography
por: Zhao, Yang, et al.
Publicado: (2016)

Unsupervised feature extraction of anterior chamber OCT images for ordering and classification
por: Amil, Pablo, et al.
Publicado: (2019)

Discriminatively Unsupervised Learning Person Re-Identification via Considering Complicated Images
por: Quan, Rong, et al.
Publicado: (2023)

An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features
por: Perrinet, Laurent U.
Publicado: (2019)

Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records
por: Bolt, Hikaru, et al.
Publicado: (2023)

Correction: Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data
por: Lasko, Thomas A., et al.
Publicado: (2013)

Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning
por: Längkvist, Martin, et al.
Publicado: (2013)

Classification and Feature Extraction Using Supervised and Unsupervised Machine Learning Approach for Broiler Woody Breast Myopathy Detection
por: Siddique, Aftab, et al.
Publicado: (2022)

Meibography guided intraductal meibomian gland probing using real-time infrared video feed
por: Maskin, Steven L, et al.
Publicado: (2020)

Changes in Noncontact Meibography and Noninvasive Tear Break-Up Time Test with Contact Lenses Usage
por: Fattoh, Reham, et al.
Publicado: (2022)

Relationship between meibomian gland loss in infrared meibography and meibum quality in dry eye patients
por: Ha, Minji, et al.
Publicado: (2022)

A simple technique of meibography for morphological and functional evaluation of meibomian glands in dry eye conditions
por: Mounika, Vala, et al.
Publicado: (2023)

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches
por: Shia, Wei-Chung, et al.
Publicado: (2021)

Natural computing for unsupervised learning
por: Li, Xiangtao, et al.
Publicado: (2018)

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
por: Chandra, Rohitash, et al.
Publicado: (2023)

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting
por: Lu, Alex X., et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_b1u0n7o1fsmgrlok6h0gd6hv2i