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[HTML][HTML] Deep learning for chest X-ray analysis: A survey
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …
segmentation models based on convolutional neural networks. Despite the new …
Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation
Training deep convolutional neural networks usually requires a large amount of labeled
data. However, it is expensive and time-consuming to annotate data for medical image …
data. However, it is expensive and time-consuming to annotate data for medical image …
Transformation-consistent self-ensembling model for semisupervised medical image segmentation
A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …
data, which is often expensive and time consuming to collect. This article presents a new …
Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …
in tackling the COVID-19. Although the convolutional neural network has great potential to …
Semi-supervised medical image classification with relation-driven self-ensembling model
Training deep neural networks usually requires a large amount of labeled data to obtain
good performance. However, in medical image analysis, obtaining high-quality labels for the …
good performance. However, in medical image analysis, obtaining high-quality labels for the …
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …
based approaches usually require large amounts of well-annotated data, which can be …
Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification
The clinical deployment of artificial intelligence (AI) applications in medical imaging is
perhaps the greatest challenge facing radiology in the next decade. One of the main …
perhaps the greatest challenge facing radiology in the next decade. One of the main …
3d semi-supervised learning with uncertainty-aware multi-view co-training
While making a tremendous impact in various fields, deep neural networks usually require
large amounts of labeled data for training which are expensive to collect in many …
large amounts of labeled data for training which are expensive to collect in many …
Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation
In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for various imaging related tasks such as artificial image generation to support AI training …
for various imaging related tasks such as artificial image generation to support AI training …