Pneumonia Identification with Self-supervised Learning and Transfer Learning
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Nine of the innate relationship studies designed their pre-text task based on different image transformations, including rotation prediction, horizontal flip prediction, reordering shuffled slices, and patch order prediction.
“Nine of these studies designed their innate relationship pre-text task based on different image transformations, including rotation prediction44–47, horizontal flip prediction48, reordering shuffled slices49, and patch order prediction46,50–52.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [47]
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · European Conference on Computer Vision · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,803 · see citing works
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477 · see citing works
Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054 · see citing works
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01829-7 · Cited by 1,142 · see citing works
Iizuka, S., Simo‐Serra, E., & Ishikawa, H. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925974 · Cited by 816 · see citing works
Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100412 · Cited by 691 · see citing works
Real-time user-guided image colorization with learned deep priors
Zhang, R., Zhu, J.-Y., Isola, P., Geng, X., Lin, A. S., Yu, T., & Efros, A. A. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073703 · Cited by 591 · see citing works
Breast cancer histopathological image classification using a hybrid deep neural network
Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., Rao, X., Zheng, C.-H., & Zhang, F. (2019) · Methods · doi:10.1016/j.ymeth.2019.06.014 · Cited by 352 · see citing works
Heart Coronary Artery Segmentation and Disease Risk Warning Based on a Deep Learning Algorithm
Xiao, C., Li, Y., & Jiang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3010800 · Cited by 58 · see citing works
Residual Convolutional Neural Network for Cardiac Image Segmentation and Heart Disease Diagnosis
Liu, T., Tian, Y., Zhao, S., Huang, X., & Wang, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2991424 · Cited by 36 · see citing works
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
- Authors
- Long, Y.
- Affiliations
- Chongqing University (CN)
- Corresponding author
- Long, Yuting
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 627-635
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3156530482
- Cited by
- 2
- References
- 14
- Citations by year
- 2025: 1 · 2023: 1
- FWCI
- 0.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Epidemiology › Pneumonia and Respiratory Infections
- Keywords
- pneumonia detection, self-supervised learning, transfer learning, chest X-ray images, CT images, convolutional neural networks, semantic feature extraction, medical image classification
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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