Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine

S Roy, T Meena, SJ Lim - Diagnostics, 2022 - mdpi.com
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

CD Pain, GF Egan, Z Chen - European Journal of Nuclear Medicine and …, 2022 - Springer
Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …

PET image denoising based on denoising diffusion probabilistic model

K Gong, K Johnson, G El Fakhri, Q Li, T Pan - European Journal of …, 2024 - Springer
Purpose Due to various physical degradation factors and limited counts received, PET
image quality needs further improvements. The denoising diffusion probabilistic model …

An improved YOLOv5-based underwater object-detection framework

J Zhang, J Zhang, K Zhou, Y Zhang, H Chen, X Yan - Sensors, 2023 - mdpi.com
To date, general-purpose object-detection methods have achieved a great deal. However,
challenges such as degraded image quality, complex backgrounds, and the detection of …

[HTML][HTML] [18F] FDG-PET/CT radiomics and artificial intelligence in lung cancer: technical aspects and potential clinical applications

R Manafi-Farid, E Askari, I Shiri, C Pirich… - Seminars in nuclear …, 2022 - Elsevier
Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

M Fallahpoor, S Chakraborty, B Pradhan… - Computer methods and …, 2024 - Elsevier
Positron emission tomography/computed tomography (PET/CT) is increasingly used in
oncology, neurology, cardiology, and emerging medical fields. The success stems from the …

New PET technologies–embracing progress and pushing the limits

N Aide, C Lasnon, A Kesner, CS Levin, I Buvat… - European journal of …, 2021 - Springer
Thanks to companies' research and development processes, frequently involving fruitful
partnerships with academic centres, and what could be acknowledged as welcome …

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

M Azeem, S Javaid, RA Khalil, H Fahim, T Althobaiti… - Bioengineering, 2023 - mdpi.com
Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount
of raw data into beneficial medical decisions for treatment and care has increased in …

Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans

A Mehranian, SD Wollenweber, MD Walker… - European Journal of …, 2022 - Springer
Purpose To improve the quantitative accuracy and diagnostic confidence of PET images
reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image …

Spach Transformer: Spatial and channel-wise transformer based on local and global self-attentions for PET image denoising

SI Jang, T Pan, Y Li, P Heidari, J Chen… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Position emission tomography (PET) is widely used in clinics and research due to its
quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR) …