Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions
MM Taye - Computation, 2023 - mdpi.com
Convolutional neural networks (CNNs) are one of the main types of neural networks used for
image recognition and classification. CNNs have several uses, some of which are object …
image recognition and classification. CNNs have several uses, some of which are object …
[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine
P Lee, S Bubeck, J Petro - New England Journal of Medicine, 2023 - Mass Medical Soc
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Mapping single-cell data to reference atlases by transfer learning
Large single-cell atlases are now routinely generated to serve as references for analysis of
smaller-scale studies. Yet learning from reference data is complicated by batch effects …
smaller-scale studies. Yet learning from reference data is complicated by batch effects …
Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Hands-on Bayesian neural networks—A tutorial for deep learning users
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …
challenging problems. However, since deep learning methods operate as black boxes, the …
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …
death cases and affected all sectors of human life. With gradual progression of time, COVID …
Application of artificial intelligence in wearable devices: Opportunities and challenges
D Nahavandi, R Alizadehsani, A Khosravi… - Computer Methods and …, 2022 - Elsevier
Background and objectives: Wearable technologies have added completely new and fast
emerging tools to the popular field of personal gadgets. Aside from being fashionable and …
emerging tools to the popular field of personal gadgets. Aside from being fashionable and …