Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Shortage of fully annotated datasets has been a limiting factor in developing deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …
based image segmentation algorithms and the problem becomes more pronounced in multi …
SaliencyGAN: Deep learning semisupervised salient object detection in the fog of IoT
In modern Internet of Things (IoT), visual analysis and predictions are often performed by
deep learning models. Salient object detection (SOD) is a fundamental preprocessing for …
deep learning models. Salient object detection (SOD) is a fundamental preprocessing for …
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging--Mini Review, Comparison and Perspectives
Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When
compared to other image modalities, it has advantages such as the absence of radiation …
compared to other image modalities, it has advantages such as the absence of radiation …
Vessel contour detection in intracoronary images via bilateral cross-domain adaptation
Vessel contour detection (VCD) in intravascular images is important for the quantitative
assessment of vessels. However, it is still a challenging task due to a high degree of …
assessment of vessels. However, it is still a challenging task due to a high degree of …
Learning the implicit strain reconstruction in ultrasound elastography using privileged information
Quasi-static ultrasound elastography is an importance imaging technology to assess the
conditions of various diseases through reconstructing the tissue strain from radio frequency …
conditions of various diseases through reconstructing the tissue strain from radio frequency …
Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images
Abstract Background Quantizing the Breast Imaging Reporting and Data System (BI-RADS)
criteria into different categories with the single ultrasound modality has always been a …
criteria into different categories with the single ultrasound modality has always been a …
Explainable COVID-19 infections identification and delineation using calibrated pseudo labels
The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh
challenges over the past two years. During this COVID-19 pandemic, there has been a need …
challenges over the past two years. During this COVID-19 pandemic, there has been a need …
DCNet: Densely connected deep convolutional encoder–decoder network for nasopharyngeal carcinoma segmentation
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to
radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor …
radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor …
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …
collaboration across multiple centres while ensuring data privacy and security are not …
An end-to-end mammogram diagnosis: A new multi-instance and multiscale method based on single-image feature
Mammography is the most common modality used in breast cancer detection. Most
diagnostic mammography studies, however, are based on single-image training with little …
diagnostic mammography studies, however, are based on single-image training with little …