The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …

Although having achieved great success in medical image segmentation, deep
convolutional neural networks usually require a large dataset with manual annotations for …

Fully-supervised deep learning segmentation models are inflexible when encountering new
unseen semantic classes and their fine-tuning often requires significant amounts of …

Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography
(CT) scans plays an important role in the quantitative management of patients. Most of the …

Recent work has shown that label-efficient few-shot learning through self-supervision can
achieve promising medical image segmentation results. However, few-shot segmentation …

The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to
research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques …