Abstract
No public abstract is available from the verified source records.
Nneka Ede, Imelda Vetter, Camryn Schroeder, Ammar M. Ahmed
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
unpaywall
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
Kept as external metadata until matched
10.1109/icaaic53929.2022.9792650
10.1109/icaaic53929.2022.9792650
10.1109/icees51510.2021.9383682
10.1109/icees51510.2021.9383682
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
10.1186/s12880-021-00551-1 · 2021
10.1109/autocom60220.2024.10486133
10.1109/autocom60220.2024.10486133
10.1109/ctceec.2017.8455029
10.1109/ctceec.2017.8455029
Unresolved referenced work
Kept as external metadata until matched
Skin disease classification using neural network
10.2174/1573405615666190422152926 · 2020
Skin diseases detection using LBP and WLD: An ensembling approach
10.1007/s42979-023-02395-8 · 2023
Vitiligo: A review
10.1159/000506103 · 2020
10.1109/iccubea54992.2022.10010801
10.1109/iccubea54992.2022.10010801
Appearance of aging signs in differently pigmented facial skin by a novel imaging system
10.1111/jocd.12806 · 2019
Prediction of clinical response to excimer laser treatment in vitiligo by using neural network models
10.1159/000225934 · 2009
Objective assessment of skin repigmentation using a multilayer perceptron
10.4103/jmss.jmss_52_18 · 2019
Checklist for evaluation of image-based artificial intelligence reports in dermatology: clear derm consensus guidelines from the International Skin Imaging Collaboration Artificial Intelligence Working Group
10.1001/jamadermatol.2021.4915 · 2022
10.1109/cvpr.2015.7298886
10.1109/cvpr.2015.7298886
10.1109/icacc.2013.48
10.1109/icacc.2013.48
Use of artificial intelligence in dermatology
10.4103/ijd.ijd_418_20 · 2020
Skin lesion classification and detection using machine learning techniques: a systematic review
2023
Unresolved referenced work
Kept as external metadata until matched
Artificial intelligence techniques for diagnosing and quantifying vitiligo
2024
Unresolved referenced work
Kept as external metadata until matched
Deep learning-based segmentation of lesions from wide-field vitiligo images
10.1142/s0218001424570076 · 2024
Diversity in machine learning: a systematic review of text-based diagnostic applications
10.1055/s-0042-1749119 · 2022
Prevalence of vitiligo among adults in the United States
10.1001/jamadermatol.2021.4724 · 2022
10.1007/978-981-97-0065-3_14
10.1007/978-981-97-0065-3_14
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Challenges of artificial intelligence in medicine and dermatology
10.1016/j.clindermatol.2023.12.013 · 2024
A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions
10.21037/atm-22-1738 · 2022
10.1109/icacci.2018.8554897
10.1109/icacci.2018.8554897
Parametric modeling of narrowband UV-B phototherapy for vitiligo using a novel quantitative tool: the vitiligo area scoring index
10.1001/archderm.140.6.677 · 2004
Evaluation of facial vitiligo severity with a mixed clinical and artificial intelligence approach
10.1016/j.jid.2023.07.014 · 2024
Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities
2024
Extensions of the external validation for checking learned model interpretability and generalizability
10.1016/j.patter.2020.100129 · 2020
Vitil-IA®: Revolutionizing facial vitiligo scoring with AI algorithm
2025
Unresolved referenced work
Kept as external metadata until matched
Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices
10.1001/jamanetworkopen.2021.7249 · 2021
Dermx: an end-to-end framework for explainable automated dermatological diagnosis
10.1016/j.media.2022.102647 · 2023
10.1007/978-3-031-38747-0_10
10.1007/978-3-031-38747-0_10
Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability
10.1038/s41598-024-59436-2 · doi-reference
Poisson-based image editing for semi-supervised vitiligo lesion segmentation with limited annotations
10.1016/j.compbiomed.2023.107320 · doi-reference
Classification of skin disease using transfer learning in convolutional neural networks
10.46338/ijetae0423_01 · doi-reference
10.1016/b978-0-12-824054-0.00007-1
10.1016/b978-0-12-824054-0.00007-1 · doi-reference
Automated scoring of vitiligo using superpixel-generated computerized digital image analysis of clinical photographs: A novel and consistent way to score vitiligo
10.1111/bjd.16563 · doi-reference
Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool
10.1186/s12880-015-0068-x · doi-reference
An automatic dermatology detection system based on deep learning and computer vision
10.1109/access.2023.3340735 · doi-reference
10.1016/b978-0-12-823817-2.00013-9
10.1016/b978-0-12-823817-2.00013-9 · doi-reference
Objective assessment of vitiligo with a computerised digital imaging analysis system
10.1111/ajd.12247 · doi-reference
Some studies in machine learning using the game of checkers
10.1147/rd.33.0210 · doi-reference
10.1007/978-3-030-32022-5_36
10.1007/978-3-030-32022-5_36 · doi-reference
10.1063/5.0109172
10.1063/5.0109172 · doi-reference
10.1109/icaiccit60255.2023.10465712
10.1109/icaiccit60255.2023.10465712 · doi-reference
10.1109/icece.2014.7026918
10.1109/icece.2014.7026918 · doi-reference
10.1109/iccsp60870.2024.10543467
10.1109/iccsp60870.2024.10543467 · doi-reference
A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases
10.1111/jdv.16967 · doi-reference
10.1109/isctech60480.2023.00092
10.1109/isctech60480.2023.00092 · doi-reference
Computerised image analysis of vitiligo lesion: Evaluation using manually defined lesion areas
10.1111/j.1600-0846.2011.00610.x · doi-reference
10.1145/3328833.3328862
10.1145/3328833.3328862 · doi-reference
10.1109/cbms49503.2020.00104
10.1109/cbms49503.2020.00104 · doi-reference
10.1007/978-3-031-16431-6_68
10.1007/978-3-031-16431-6_68 · doi-reference
An effective vitiligo intelligent classification system
10.1007/s12652-020-02357-5 · doi-reference
10.1109/isbi45749.2020.9098682
10.1109/isbi45749.2020.9098682 · doi-reference
Artificial intelligence for the classification of pigmented skin lesions in populations with skin of color: a systematic review
10.1159/000530225 · doi-reference
A deep learning system for differential diagnosis of skin diseases
10.1038/s41591-020-0842-3 · doi-reference
10.1007/978-3-030-24265-7_19
10.1007/978-3-030-24265-7_19 · doi-reference
Scalable skin lesion multi-classification recognition system
10.32604/cmc.2020.07039 · doi-reference
Bridging the gap between vitiligo segmentation and clinical scores
10.1109/jbhi.2023.3342069 · doi-reference
Segmenting vitiligo on clinical face images using CNN trained on synthetic and internet images
10.1109/jbhi.2021.3055213 · doi-reference
A comprehensive survey on design and application of autoencoder in deep learning
10.1016/j.asoc.2023.110176 · doi-reference
10.1017/cbo9781139176224.006
10.1017/cbo9781139176224.006 · doi-reference
Vitiligo area scoring index and vitiligo european task force assessment: reliable and responsive instruments to measure the degree of depigmentation in vitiligo
10.1111/bjd.13432 · doi-reference
Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images
10.1111/srt.12920 · doi-reference
Disease severity indexes and treatment evaluation criteria in vitiligo
10.1155/2011/750342 · doi-reference