Results 191 to 200 of about 117,293 (275)

Controlled comparative study of YOLOv8-Pose, YOLOv11-Pose, and Detectron2 for vertebrae detection and keypoint estimation. [PDF]

PLoS One
Bansal A, Kubíček J, Garg S.
europepmc   +1 more source

Visual Ensemble Analysis With Deep Learning Prediction for Studying the Effect of Tissue Properties on Radiofrequency Ablation

Computer Graphics Forum, EarlyView.
We present an interactive visual analysis tool to study how patient‐specific tissue properties influence radiofrequency ablation outcomes. Using a deep‐learning surrogate model, we predict ablation volumes for unseen parameter settings with accuracy superior to interpolation, supporting improved treatment planning. Abstract Radiofrequency (RF) ablation
R. Sabbagh Gol, M. Evers, K. Heimes, T. Gerrits, S. Gyawali, D. Sinden, T. Preusser, L. Linsen   +7 more
wiley   +1 more source

Pixel-level understanding of a world in motion within a neural encoding framework. [PDF]

Sci Rep
Gamal M, Rashad M, Ehab E, Eldawlatly S, Siam M.   +4 more
europepmc   +1 more source

Multi‐angle, cross‐domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae)

Cladistics, EarlyView.
Abstract Automated insect identification systems hold significant value for biodiversity monitoring, pest management, citizen science initiatives and systematic studies, particularly in an era of declining expertise in insect taxonomy. However, current deep learning approaches often rely on standardized specimen photos from limited‐angles and ...
Xinkai Wang, Huaiyu Liu, Zhuo Chen, Yisheng Zhao, Yingqi Liu, Haoyang Xiong, Yuange Duan, Fang Song, Wanzhi Cai, Xuankun Li, Hu Li   +10 more
wiley   +1 more source

Efficient and explainable histopathology for cancer detection using dual-teacher distillation and integrated gradients. [PDF]

Front Med (Lausanne)
Ahmad K, Arif S, Hanif M, Alturki N, Asghar MN, Shah MA.   +5 more
europepmc   +1 more source

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis. [PDF]

Mach Learn Health
Bench C, Pfeffer O, Desai V, Moulaeifard M, Coquelin L, Charlton PH, Strodthoff N, Hegemann N, Aston PJ, Thompson A.   +9 more
europepmc   +1 more source

Artificial Intelligence in the Detection of Clinically Negotiable Second Mesio‐Buccal Canals in Periapical Images of Maxillary Molars

International Endodontic Journal, EarlyView.
ABSTRACT Aim Artificial intelligence (AI) has the potential to aid clinicians in assessing case difficulty in endodontics. The objectives of this study were to develop and validate deep learning models for the detection of clinically negotiable MB2 canals in periapical images of maxillary first and second molars, and to compare the performance of AI ...
Seyed AmirHossein Ourang, Fatemeh Sohrabniya, Soroush Sadr, Jane Lee, Noreen Ramzy, Alan S. Law, Ernest W. N. Lam, Asma Ahmed Khan, Ali Nosrat   +8 more
wiley   +1 more source

A multimodal feature fusion with deep representation learning approach for polycystic ovary syndrome diagnosis using ultrasound images. [PDF]

Sci Rep
Kranthi S, Sandeep Y, Pranathi K, Laxmi Lydia E, Joshi GP, Cho W.   +5 more
europepmc   +1 more source

Mamba‐Based Deep Learning Model for Automated Periapical Index Classification Using Periapical Radiographs and Clinical Metadata

International Endodontic Journal, EarlyView.
ABSTRACT Aim Apical periodontitis (AP) diagnosis primarily relies on periapical radiographs (PRs) and the Periapical Index (PAI) scoring system. However, existing automated approaches often simplify PAI into binary categories or ignore essential clinical metadata, limiting diagnostic performance and applicability.
Jiyun Lee, Yudam Park, Sunil Kim, Ikbeom Jang   +3 more
wiley   +1 more source