Results 161 to 170 of about 118,357 (248)

Exploring a Novel Conv‐Transformer Network for Multi‐Modality Heart Segmentation

iRADIOLOGY, EarlyView.
We propose SFAM‐TransUnet for multimodality whole heart segmentation, a novel deep learning framework combining CNNs and transformers. Extensive experiments conducted on the clinical Multi‐Modality Whole Heart Segmentation datasets demonstrate that SFAM‐TransUnet outperforms various alternative methods.
Youyou Ding, Hao Dang, Jiayi Luo, Xiaoyu Zhuo, Ningyu Huang, Junsheng Xiao, Zongwang Lv   +6 more
wiley   +1 more source

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, EarlyView.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Distinguishing lung cancer from atypical tuberculosis: Deep transfer learning and imaging omics features

Journal of Intelligent Medicine, EarlyView.
The 3D‐ResNet model demonstrates superior discriminative power in differentiating lung cancer from atypical tuberculosis by leveraging deep omics features derived from volumetric lung cancer imaging, outperforming conventional clinical and radiomic analyses.
Yi Wu, Siyi Li, Zhiping Long, Yanjie Jia, Yue Yu, Bing Pei, Heli Yuan, Yuhua Hao, Zhiyun Jiang, Lei Cao, Kezheng Wang, Fan Wang   +11 more
wiley   +1 more source

Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

Sustainable cities and society, 2020
Mohamed Loey, Gunasekaran Manogaran, M. Taha, Nour Eldeen M. Khalifa   +3 more
semanticscholar   +1 more source

Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs

Journal of Medical Radiation Sciences, EarlyView.
This study evaluated DenseNet121 and YOLOv8 neural networks in detecting suboptimal chest x‐rays for quality control. Through training, validation, and testing, both AI models effectively classified chest X‐ray quality, highlighting the potential to provide radiographers with feedback to enhance image quality.
Emily Huanke Liu, Daniel Carrion, Mohamed Khaldoun Badawy   +2 more
wiley   +1 more source

Artificial Intelligence‐Based Quantification to Assess the Automatic Psoriasis Area and Severity Index

JEADV Clinical Practice, EarlyView.
ABSTRACT Background Psoriasis is a chronic inflammatory skin condition affecting millions globally, traditionally assessed via the Psoriasis Area and Severity Index (PASI). Despite its widespread use, PASI suffers from subjectivity, limited sensitivity for small lesions, and high interobserver variability.
Taig Mac Carthy, Daniel Dagnino, Alfonso Medela, Gerardo Fernández, Andy Aguilar, Antonio Martorell, Pedro Gómez‐Tejerina, Gastón Roustán‐Gullón   +7 more
wiley   +1 more source

Enabling image optimisation and artificial intelligence technologies for better Internet of Things framework to predict COVID

IET Networks, EarlyView., 2022
Abstract Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework.
Noor M Allayla, Farah Nazar Ibraheem, Refed Adnan Jaleel   +2 more
wiley   +1 more source

3D imaging‐based AI models outperform demographic models and excel in tibial sizing compared with 2D models in total knee arthroplasty planning: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose Accurate preoperative implant sizing is a critical component of successful total knee arthroplasty (TKA). Artificial intelligence (AI) has emerged as a promising tool for enhancing preoperative planning. This is achieved through predictive modelling based on different input modalities, including computed tomography (CT), plain ...
Randa Elsheikh, Zainab Aqeel Khan, George Mihai Avram, Rolf Huegli, Andrej M. Nowakowski, Michael T. Hirschmann   +5 more
wiley   +1 more source

Optical Coherence Tomography for High‐Precision Industrial Inspection in Industry 4.0: Advances, Challenges, and Future Trends

Laser &Photonics Reviews, EarlyView.
This review examines how optical coherence tomography transforms industrial inspection by delivering real‐time, micrometer‐resolution, depth‐resolved imaging. It surveys applications across display manufacturing, thin films, microelectronics, laser processing, and coatings, evaluates performance against conventional techniques, and highlights emerging ...
Nipun Shantha Kahatapitiya, Sm Abu Saleah, Daewoon Seong, Naresh Kumar Ravichandran, Sangyeob Han, Ruchire Eranga Wijesinghe, Mansik Jeon, Jeehyun Kim   +7 more
wiley   +1 more source

Jointly Learned 3D Non‐Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. Methods Pseudo‐fully sampled neurovascular 4D flow data (N = 40) and a simulation framework were used to learn phase encoding locations, wave readout parameters, and model‐based reconstruction network (MoDL) for a rapid 3D PC scan (
Chenwei Tang, Brock W. Jolicoeur, James Rice, Caroline A. Doctor, Zaynab S. Yardim, Leonardo A. Rivera‐Rivera, Laura B. Eisenmenger, Kevin M. Johnson   +7 more
wiley   +1 more source