Ultrasound (US) is one of the most widely used imaging modalities in medical diagnosis. It
has the advantages of real-time, low cost, noninvasive nature, and easy to operate …

Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …

Many known supervised deep learning methods for medical image segmentation suffer an
expensive burden of data annotation for model training. Recently, few-shot segmentation …

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-
supervised models have already achieved outstanding performance with dense pixel-level …

Active learning (AL) aims to select valuable samples for labeling from an unlabeled sample
pool to build a training dataset with minimal annotation cost. Traditional methods always …

Pulmonary Embolism (PE) represents a thrombus (“blood clot”), usually originating from a
lower extremity vein, that travels to the blood vessels in the lung, causing vascular …

The two-way information exchange between customers and the utility in smart grids enables
demand-response programs of customers and the integration of distributed renewable …

Image segmentation tasks based on deep learning usually require a large number of
labeled samples to obtain great performance of Convolutional Neural Networks (CNNs) …

High performance of deep learning models on medical image segmentation greatly relies on
large amount of pixel-wise annotated data, yet annotations are costly to collect. How to …