Medical Imaging
Deep learning for reading and interpreting medical images, such as ultrasound, to support diagnosis and clinical decisions.
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Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study
L Wang, Y Zhang, M Chignell, B Shan, K Sheehan, FA Razak, AA Verma. Cited by 32
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Patient Safety Challenges in the Pandemic: Applying Human Factors Principles to Embed Patient Safety and Experience at the Clinical Front Line
M Chignell, TNT Hall, L Liu, M Kastner, FA Razak
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Overview and Application-Driven Motivations of Evolutionary Multitasking
L Feng, A Gupta, KC Tan, YS Ong
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A meta-review of psychological resilience during COVID-19
K Seaborn, K Henderson, J Gwizdka, M Chignell. Cited by 18
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Mutual Information and Ensemble Based Feature Recommender for Renal Cancer Stage Classification
A Dey, D Goswami, R Roy, S Ghosh, YS Zhang, JH Chan
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Environmental measurements and genetic effects for cancer survival integration data
A Iuliano, A Occhipinti
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Lean evolutionary reinforcement learning by multitasking with importance sampling
N Zhang, A Gupta, Z Chen, YS Ong. Cited by 1
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Intelligent Data Engineering and Automated Learning–IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings
H Yin, D Camacho, P Tino. Cited by 2
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A multifactorial differential evolution with hybrid global and local search strategies
M Xu, Y Zheng, YS Ong, Z Zhu, X Ma. Cited by 4
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Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data
JH Chan, C Li. Cited by 8
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Applying the stimulus organism response framework to explain student’s academic self-concept in online learning during the COVID-19 pandemic
R Rohan, FLI Dutsinma, D Pal, S Funilkul. Cited by 8
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Automatic Text Summarization of COVID-19 Scientific Research Topics Using Pre-trained Models from Hugging Face
S Ontoum, JH Chan. Cited by 12