Reference health
An Interpretable Deep Learning Approach for Automatic Diagnosis of Fungal Keratitis and Acanthamoeba Keratitis Based on an Introduced Database of in Vivo Confocal Microscopic Images
19/19 checkable references clean · checked 2026-10-18
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
17 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 19 checked references that resolve
resolves10.1097/ICL.0b013e318157c49d
Epidemiology of Contact Lens–Related Inflammation and Microbial Keratitis: A 20-year Perspective
resolves10.1167/iovs.16-20110
Evaluation of Corneal Cross-Linking for Treatment of Fungal Keratitis: Using Confocal Laser Scanning Microscopy on an Ex Vivo Human Corneal Model
resolves10.1371/journal.pntd.0007964
Fungal diseases as neglected pathogens: A wake-up call to public health officials
resolves10.1051/parasite/2015010
An update on
<i>Acanthamoeba</i>
keratitis: diagnosis, pathogenesis and treatment
resolves10.1136/bjo.2009.175083
Diagnostic accuracy of microbial keratitis with in vivo scanning laser confocal microscopy
resolves10.1016/j.ophtha.2018.02.037
A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography
resolves10.1016/j.cell.2018.02.010
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
resolves10.1097/ICO.0000000000002279
Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy
resolves10.1145/3432291.3432310
Deep Learning for Three Types of Keratitis Classification based on Confocal Microscopy Images
resolves10.1080/08164622.2021.1932430
A triad of microscopes for rapid and proper diagnosis of infectious keratitis
resolves10.1016/j.survophthal.2012.12.003
Quantitative analysis of in vivo confocal microscopy images: A review
resolves10.1167/iovs.10-7048
Inflammation and the Nervous System: The Connection in the Cornea in Patients with Infectious Keratitis
The 17 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref10
no DOI — not checkedAutomatic feature learning for glaucoma detection based on deep learning
no DOI — not checkedDevelopment of machine learning models for diagnosis of glaucoma
no DOI — not checkedDeep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images
no DOI — not checkedref20
no DOI — not checkedref21
no DOI — not checkedref23
no DOI — not checkedData augmentation for improving deep learning in image classification problem
no DOI — not checkedDeep Residual Learning for Image Recognition
no DOI — not checkedCSPNet: A new backbone that can enhance learning capability of CNN
no DOI — not checkedAggregated Residual Transformations for Deep Neural Networks
no DOI — not checkedref29
no DOI — not checkedUnderstanding the difficulty of training deep feedforward neural networks
no DOI — not checkedref32
no DOI — not checkedClinical in vivo confocal microscopy of the human cornea in health and disease
no DOI — not checkedref35
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