Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
COVID-19 diagnosis-based deep learning approaches for COVIDx dataset: A preliminary survey
This chapter presents a comprehensive review of the utilization of deep learning (DL)
approaches to COVID-19 identification and lung segmentation. It also presents a review of …
approaches to COVID-19 identification and lung segmentation. It also presents a review of …
From concept to implementation: the data-centric development process for AI in industry
We examine the paradigm of data-centric artificial intelligence (DCAI) as a solution to the
obstacles that small and medium-sized enterprises (SMEs) face in adopting AI. While the …
obstacles that small and medium-sized enterprises (SMEs) face in adopting AI. While the …
Pre-clustering active learning method for automatic classification of building structures in urban areas
Identifying the structures of buildings in urban areas is a prerequisite for robust urban
planning and regeneration. Owing to the diverse structural designs of urban buildings …
planning and regeneration. Owing to the diverse structural designs of urban buildings …
Active learning performance in labeling radiology images is 90% effective
To train artificial intelligence (AI) systems on radiology images, an image labeling step is
necessary. Labeling for radiology images usually involves a human radiologist manually …
necessary. Labeling for radiology images usually involves a human radiologist manually …
Medical image labeling via active learning is 90% effective
Medical imaging AI models require large image datasets that have been labeled, or
annotated, by medical professionals who are a scarce and expensive resource. Manual …
annotated, by medical professionals who are a scarce and expensive resource. Manual …
Analytic mutual information in bayesian neural networks
JO Woo - 2022 IEEE International Symposium on Information …, 2022 - ieeexplore.ieee.org
Bayesian neural networks have successfully designed and optimized a robust neural
network model in many application problems, including uncertainty quantification. However …
network model in many application problems, including uncertainty quantification. However …
Active Learning
KC Santosh, S Nakarmi - Active Learning to Minimize the Possible Risk of …, 2023 - Springer
Active Learning—Review | SpringerLink Skip to main content Advertisement SpringerLink
Account Menu Find a journal Publish with us Track your research Search Cart Book cover Active …
Account Menu Find a journal Publish with us Track your research Search Cart Book cover Active …
Towards Generalist Medical Imaging Artificial Intelligence Using Multimodal Self-Supervised Learning
SC Huang - 2024 - search.proquest.com
Artificial intelligence (AI) has shown great promise in revolutionizing medical imaging
interpretation, but the development of clinically useful AI models remains challenging. In this …
interpretation, but the development of clinically useful AI models remains challenging. In this …
Active learning in deep convolutional neural networks for image segmentation
I Schwartz, W Åkvist - 2022 - odr.chalmers.se
The sitting position and seat belt orientation of passengers in automobiles can be crucial in
the event of a collision. In order to warn passengers of unsafe positions, deep learning …
the event of a collision. In order to warn passengers of unsafe positions, deep learning …