[HTML][HTML] A review of predictive and contrastive self-supervised learning for medical images
WC Wang, E Ahn, D Feng, J Kim - Machine Intelligence Research, 2023 - Springer
Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …
progressing significantly on computer vision tasks. But, the application of deep learning in …
Twin broad learning system for fault diagnosis of rotating machinery
As rotating machines are more and more widely used in modern equipment, their fault
diagnosis is important to guarantee the instrument's reliability and safety. Although …
diagnosis is important to guarantee the instrument's reliability and safety. Although …
QCLR: Quantum-LSTM contrastive learning framework for continuous mental health monitoring
Abstract Technologies such as Artificial Intelligence, Machine Learning, and Internet of
Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large …
Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large …
Pre-training auto-generated volumetric shapes for 3d medical image segmentation
R Tadokoro, R Yamada… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In 3D medical image segmentation, data collection and annotation costs require significant
human efforts. Moreover, obtaining training data is challenging due to privacy constraints …
human efforts. Moreover, obtaining training data is challenging due to privacy constraints …
Examining the quality of learned representations in self-supervised medical image analysis: a comprehensive review and empirical study
Abstract Medical Image Analysis (MIA) is integral to healthcare, demanding advanced
computational techniques for precise diagnostics and treatment planning. The demand for …
computational techniques for precise diagnostics and treatment planning. The demand for …
A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET
Brain tumor, abnormal cell growth within the brain, require precise segmentation to facilitate
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …
Contrastive learning-based adenoid hypertrophy grading network using nasoendoscopic image
S Zheng, X Li, M Bi, Y Wang, H Liu… - 2022 IEEE 35th …, 2022 - ieeexplore.ieee.org
Adenoid hypertrophy is a common disease in children with otolaryngology diseases.
Otolaryngologists usually use nasoendoscopy for adenoid hypertrophy screening, which is …
Otolaryngologists usually use nasoendoscopy for adenoid hypertrophy screening, which is …
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework
S Manna, R Dey, S Chakraborty - arXiv preprint arXiv:2208.00444, 2022 - arxiv.org
Applications on Medical Image Analysis suffer from acute shortage of large volume of data
properly annotated by medical experts. Supervised Learning algorithms require a large …
properly annotated by medical experts. Supervised Learning algorithms require a large …