Results 91 to 100 of about 886,852 (323)

Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Training deep learning dose prediction models for the latest cutting‐edge radiotherapy techniques, such as AI‐based nodal radiotherapy (AINRT) and Daily Adaptive AI‐based nodal radiotherapy (DA‐AINRT), is challenging due to limited data.
Hui‐Ju Wang, Austen Maniscalco, David Sher, Mu‐Han Lin, Steve Jiang, Dan Nguyen   +5 more
wiley   +1 more source

A review of artificial intelligence in brachytherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen, Richard L. J. Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang   +4 more
wiley   +1 more source

A computer architecture based on disruptive information technologies for drug management in hospitals. [PDF]

PeerJ Comput Sci, 2023
Chalmeta R, Navarro-Ruiz A, Soriano-Irigaray L.   +2 more
europepmc   +1 more source

Architecture of Computational Ecosystems

, 2022
Industry 4.0 ha transformado rápida y significativamente la industria AEC a nivel mundial desde principios de la década de 2000. La profesión profesional de la arquitectura alográfica hasta finales de la década de 1990 ha visto un cambio de paradigma hacia una práctica más autográfica después de la inclusión del algoritmo en el desarrollo ...
openaire   +1 more source

Closing the gap in plan quality: Leveraging deep‐learning dose prediction for adaptive radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re‐optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high‐quality plan within time constraints remains a common barrier.
Sean J. Domal, Austen Maniscalco, Justin Visak, Michael Dohopolski, Dominic Moon, Vladimir Avkshtol, Dan Nguyen, Steve Jiang, David Sher, Mu‐ Han Lin   +9 more
wiley   +1 more source

A Semi-Supervised Assessor of Neural Architectures [PDF]

arXiv, 2020
Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficiently value an intermediate neural architecture. But for the training of this predictor, a number of neural architectures and their corresponding real performance often have to ...
arxiv  

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Studies on deep learning dose prediction increasingly focus on 3D models with multiple input channels and data augmentation, which increases the training time and thus also the environmental burden and hampers the ease of re‐training. Here we compare 2D and 3D U‐Net models with clinical accepted plans to evaluate the appropriateness of
Rosalie Klarenberg, Nienke L. M. Bakx, Coen W. Hurkmans   +2 more
wiley   +1 more source

Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off‐the‐shelf features: A dual‐dataset study

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image‐based HPV prediction methods are hindered by high computational demands or suboptimal performance.
Junhua Chen, Yanyan Cheng, Lijun Chen, Banghua Yang   +3 more
wiley   +1 more source

Geometric and dosimetric evaluation of a commercial AI auto‐contouring tool on multiple anatomical sites in CT scans

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan, Alexandra Quinn, Patrick Horsley, Joseph Chan, Maegan Stewart, Regina Bromley, Jeremy Booth   +6 more
wiley   +1 more source

Computer Architecture with Associative Processor Replacing Last Level Cache and SIMD Accelerator [PDF]

arXiv, 2013
This study presents a novel computer architecture where a last level cache and a SIMD accelerator are replaced by an Associative Processor. Associative Processor combines data storage and data processing and provides parallel computational capabilities and data memory at the same time.
arxiv