Cargando…

Benchmarking Deep Learning Models for Tooth Structure Segmentation

A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Benchmarking (i.e., the systematic comparison of state-of-the art architectures on a specific task) may provide gu...

Descripción completa

Detalles Bibliográficos
Autores principales:	Schneider, L., Arsiwala-Scheppach, L., Krois, J., Meyer-Lueckel, H., Bressem, K.K., Niehues, S.M., Schwendicke, F.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	SAGE Publications 2022
Materias:	Research Reports
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516600/ https://www.ncbi.nlm.nih.gov/pubmed/35686357 http://dx.doi.org/10.1177/00220345221100169

Ejemplares similares

Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning
por: Rohrer, Csaba, et al.
Publicado: (2022)

Machine Learning in Dentistry: A Scoping Review
por: Arsiwala-Scheppach, Lubaina T., et al.
Publicado: (2023)

Predicting mortality in the very old: a machine learning analysis on claims data
por: Krasowski, Aleksander, et al.
Publicado: (2022)

Impact of Image Context on Deep Learning for Classification of Teeth on Radiographs
por: Krois, Joachim, et al.
Publicado: (2021)

Deep learning for cephalometric landmark detection: systematic review and meta-analysis
por: Schwendicke, Falk, et al.
Publicado: (2021)

Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs—A Retrospective Study
por: Cejudo Grano de Oro, José Eduardo, et al.
Publicado: (2022)

Long-term costs of post-restorations: 7-year practice-based results from Germany
por: Schwendicke, Falk, et al.
Publicado: (2020)

Costs for Statutorily Insured Dental Services in Older Germans 2012–2017
por: Krasowski, Aleksander, et al.
Publicado: (2021)

Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs
por: Büttner, Martha, et al.
Publicado: (2023)

Evaluating Modeling and Validation Strategies for Tooth Loss
por: Krois, J., et al.
Publicado: (2019)

Classification of Dental Radiographs Using Deep Learning
por: Cejudo, Jose E., et al.
Publicado: (2021)

Federated Learning in Dentistry: Chances and Challenges
por: Rischke, R., et al.
Publicado: (2022)

Artificial Intelligence for Caries Detection: Value of Data and Information
por: Schwendicke, F., et al.
Publicado: (2022)

Comparing different deep learning architectures for classification of chest radiographs
por: Bressem, Keno K., et al.
Publicado: (2020)

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly
por: Meddeb, Aymen, et al.
Publicado: (2022)

Deep learning for accurately recognizing common causes of shoulder pain on radiographs
por: Grauhan, Nils F., et al.
Publicado: (2021)

Deep Learning for the Radiographic Detection of Periodontal Bone Loss
por: Krois, Joachim, et al.
Publicado: (2019)

EM-stellar: benchmarking deep learning for electron microscopy image segmentation
por: Khadangi, Afshin, et al.
Publicado: (2021)

Generalizability of deep learning models for dental image analysis
por: Krois, Joachim, et al.
Publicado: (2021)

Generalizability of Deep Learning Models for Caries Detection in Near-Infrared Light Transillumination Images
por: Holtkamp, Agnes, et al.
Publicado: (2021)

Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study
por: Mohammad-Rahimi, Hossein, et al.
Publicado: (2023)

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen
por: Meddeb, Aymen, et al.
Publicado: (2021)

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs
por: Sheng, Chen, et al.
Publicado: (2022)

Benchmark of Deep Learning models
por: Terko, Afan
Publicado: (2021)

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets
por: Kar, Anuradha, et al.
Publicado: (2022)

Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond
por: Schwendicke, F., et al.
Publicado: (2021)

Data Dentistry: How Data Are Changing Clinical Care and Research
por: Schwendicke, F., et al.
Publicado: (2021)

Precision dentistry—what it is, where it fails (yet), and how to get there
por: Schwendicke, Falk, et al.
Publicado: (2022)

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder
por: Huwiler, Marc
Publicado: (2017)

Costs and Effectiveness of Treatment Alternatives for Proximal Caries Lesions
por: Schwendicke, Falk, et al.
Publicado: (2014)

Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study
por: Herbst, Sascha Rudolf, et al.
Publicado: (2023)

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning
por: Im, Joon, et al.
Publicado: (2022)

Caries detection with tooth surface segmentation on intraoral photographic images using deep learning
por: Park, Eun Young, et al.
Publicado: (2022)

Artificial Intelligence in Dentistry: Chances and Challenges
por: Schwendicke, F., et al.
Publicado: (2020)

Can We Predict Usage of Dental Services? An Analysis from Germany 2000 to 2015
por: Schwendicke, F., et al.
Publicado: (2020)

Impact of SARS-CoV2 (Covid-19) on dental practices: Economic analysis
por: Schwendicke, Falk, et al.
Publicado: (2020)

Big Data and Complex Data Analytics: Breaking Peer Review?
por: Schwendicke, F., et al.
Publicado: (2022)

Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
por: Bressem, Keno K., et al.
Publicado: (2021)

Towards Trustworthy AI in Dentistry
por: Ma, J., et al.
Publicado: (2022)

How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers
por: Danek, Stella, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_svsinvtf4rnjidbonku9g6hc5n