Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Clip-driven universal model for organ segmentation and tumor detection
An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …
segmentation and tumor detection. However, due to the small size and partially labeled …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application
Recently, human monkeypox outbreaks have been reported in many countries. According to
the reports and studies, quick determination and isolation of infected people are essential to …
the reports and studies, quick determination and isolation of infected people are essential to …
[HTML][HTML] The future of digital health with federated learning
Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …
accurate and robust statistical models from medical data, which is collected in huge volumes …
Preparing medical imaging data for machine learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …
potential applications are vast and include the entirety of the medical imaging life cycle from …
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Labeled medical imaging data is scarce and expensive to generate. To achieve
generalizable deep learning models large amounts of data are needed. Standard data …
generalizable deep learning models large amounts of data are needed. Standard data …
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 …
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …