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 …
Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
Contrastive self-supervised learning from 100 million medical images with optional supervision
Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However …
assessment requires the creation of large sets of annotated training examples. However …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
Generating and weighting semantically consistent sample pairs for ultrasound contrastive learning
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power
in extracting lesion-related features. Building such large and well-designed medical …
in extracting lesion-related features. Building such large and well-designed medical …
Preinterventional Third-Molar Assessment Using Robust Machine Learning
JS Carvalho, M Lotz, L Rubi, S Unger… - Journal of Dental …, 2023 - journals.sagepub.com
Machine learning (ML) models, especially deep neural networks, are increasingly being
used for the analysis of medical images and as a supporting tool for clinical decision …
used for the analysis of medical images and as a supporting tool for clinical decision …
DenSplitnet: Classifier-invariant neural network method to detect COVID-19 in chest CT data
M Perumal, M Srinivas - Journal of Visual Communication and Image …, 2023 - Elsevier
Objective: COVID-19 has made an unprecedented impact on humanity. The Healthcare
sector, in an effort to curb COVID-19, could leverage Artificial Intelligence (AI) to its aid …
sector, in an effort to curb COVID-19, could leverage Artificial Intelligence (AI) to its aid …
Understanding calibration of deep neural networks for medical image classification
Abstract Background and Objective–In the field of medical image analysis, achieving high
accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence …
accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence …
Focused decoding enables 3D anatomical detection by transformers
Detection Transformers represent end-to-end object detection approaches based on a
Transformer encoder-decoder architecture, exploiting the attention mechanism for global …
Transformer encoder-decoder architecture, exploiting the attention mechanism for global …
Dive into self-supervised learning for medical image analysis: Data, models and tasks
C Zhang, Y Gu - arXiv preprint arXiv:2209.12157, 2022 - arxiv.org
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific …