Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect
Z Yin, C Yao, L Zhang, S Qi - Frontiers in Medicine, 2023 - frontiersin.org
In the past few decades, according to the rapid development of information technology,
artificial intelligence (AI) has also made significant progress in the medical field. Colorectal …
artificial intelligence (AI) has also made significant progress in the medical field. Colorectal …
The added value of artificial intelligence to LI-RADS categorization: A systematic review
Purpose The objective of this systematic review was to critically assess the available
literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and …
literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and …
An intelligent healthcare cyber physical framework for encephalitis diagnosis based on information fusion and soft-computing techniques
Viral encephalitis is a contagious disease that causes life insecurity and is considered one
of the major health concerns worldwide. It causes inflammation of the brain and, if left …
of the major health concerns worldwide. It causes inflammation of the brain and, if left …
Cxai: Explaining convolutional neural networks for medical imaging diagnostic
Deep learning models have been increasingly applied to medical images for tasks such as
lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of …
lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of …
Deep support vector machine for PolSAR image classification
The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image
classification in remote sensing is the ability to develop classifiers that can substantially …
classification in remote sensing is the ability to develop classifiers that can substantially …
Creating meaningful work in the age of AI: explainable AI, explainability, and why it matters to organizational designers
K Wulff, H Finnestrand - AI & SOCIETY, 2024 - Springer
In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to
organizations improving the customer experiences and their internal processes through …
organizations improving the customer experiences and their internal processes through …
Predicting thalassemia using deep neural network based on red blood cell indices
D Mo, Q Zheng, B Xiao, L Li - Clinica Chimica Acta, 2023 - Elsevier
Background and objective The traditional statistical screening method for thalassemia based
on red blood cell (RBC) indices is being replaced by machine learning. Here, we developed …
on red blood cell (RBC) indices is being replaced by machine learning. Here, we developed …
Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantisation
V Adarsh, GR Gangadharan - Machine Learning, 2024 - Springer
This study introduces a novel pruning approach based on explainable graph convolutional
networks, strategically amalgamating pruning and quantisation, aimed to tackle the …
networks, strategically amalgamating pruning and quantisation, aimed to tackle the …
Hybrid value-aware transformer architecture for joint learning from longitudinal and non-longitudinal clinical data
Transformer is the latest deep neural network (DNN) architecture for sequence data
learning, which has revolutionized the field of natural language processing. This success …
learning, which has revolutionized the field of natural language processing. This success …
Towards interpretable camera and lidar data fusion for autonomous ground vehicles localisation
Recent deep learning frameworks draw strong research interest in application of ego-motion
estimation as they demonstrate a superior result compared to geometric approaches …
estimation as they demonstrate a superior result compared to geometric approaches …