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Deep learning for cardiac image segmentation: a review
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
[HTML][HTML] Test-time adaptable neural networks for robust medical image segmentation
Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …
problems when the training dataset is representative of the variations expected to be …
Orthogonal annotation benefits barely-supervised medical image segmentation
Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …
[HTML][HTML] Impact of quality, type and volume of data used by deep learning models in the analysis of medical images
The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …
[HTML][HTML] Multi-modality cardiac image computing: A survey
Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …
cardiovascular diseases. It allows a combination of complementary anatomical …
A multi-attention and depthwise separable convolution network for medical image segmentation
Automatic medical image segmentation method is highly needed to help experts in lesion
segmentation. The deep learning technology emerging has profoundly driven the …
segmentation. The deep learning technology emerging has profoundly driven the …
Learning with context feedback loop for robust medical image segmentation
Deep learning has successfully been leveraged for medical image segmentation. It employs
convolutional neural networks (CNN) to learn distinctive image features from a defined pixel …
convolutional neural networks (CNN) to learn distinctive image features from a defined pixel …
MOLS-Net: Multi-organ and lesion segmentation network based on sequence feature pyramid and attention mechanism for aortic dissection diagnosis
Q Zhou, J Qin, X **ang, Y Tan, Y Ren - Knowledge-Based Systems, 2022 - Elsevier
Aortic dissection is a rapid and critical cardiovascular disease. The automatic segmentation
and detection of related organs and lesions in CT volumes of aortic dissection provide great …
and detection of related organs and lesions in CT volumes of aortic dissection provide great …
[HTML][HTML] Atlas-ISTN: joint segmentation, registration and atlas construction with image-and-spatial transformer networks
Deep learning models for semantic segmentation are able to learn powerful representations
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …