Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives
NN Zhong, HQ Wang, XY Huang, ZZ Li, LM Cao… - Seminars in Cancer …, 2023 - Elsevier
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …
Applications of deep learning to neuro-imaging techniques
Many clinical applications based on deep learning and pertaining to radiology have been
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT
R Singh, SR Digumarthy, VV Muse… - American Journal of …, 2020 - Am Roentgen Ray Soc
OBJECTIVE. The objective of this study was to compare image quality and clinically
significant lesion detection on deep learning reconstruction (DLR) and iterative …
significant lesion detection on deep learning reconstruction (DLR) and iterative …
A survey on deep learning in medical image reconstruction
E Ahishakiye, M Bastiaan Van Gijzen… - Intelligent …, 2021 - mednexus.org
Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …
at minimal cost and risk to the patients. Deep learning and its applications in medical …
Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions
Computed tomography (CT) is widely used in medical diagnosis and non-destructive
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
detection. Image reconstruction in CT aims to accurately recover pixel values from measured …
Deep learning‐based image reconstruction for different medical imaging modalities
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT)
is a mathematical process that generates images at many different angles around the …
is a mathematical process that generates images at many different angles around the …
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model …
A Barragán-Montero, A Bibal… - Physics in Medicine …, 2022 - iopscience.iop.org
The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …
the performance leap that occurred with new techniques of deep learning, convolutional …
[HTML][HTML] An overview of organs-on-chips based on deep learning
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in
biomedical and chemical research and have emerged as one of the most advanced and …
biomedical and chemical research and have emerged as one of the most advanced and …
Noninterpretive uses of artificial intelligence in radiology
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would
normally require intelligent action by a human. Much of the recent excitement about AI in the …
normally require intelligent action by a human. Much of the recent excitement about AI in the …
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …