Ultrasound imaging can vary in style/appearance due to differences in scanning equipment
and other factors, resulting in degraded segmentation and classification performance of …

Deep segmentation models that generalize to images with unknown appearance are
important for real-world medical image analysis. Retraining models leads to high latency …

Deep Neural Networks (DNNs) suffer from the performance degradation when image
appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper …

The use of deep learning in medical image analysis is hindered by insufficient annotated
data and the inability of models to generalize between different imaging settings. We …

Deep learning systems for medical image analysis have shown remarkable performance.
However performance is heavily dependent on the size and diversity of the training data as …

In ultrasound imaging, sonographers are tasked with analyzing scans for diagnostic
purposes; a challenging task, especially for novice sonographers. Deep Learning methods …

Data augmentation is a widely used and effective technique to improve the generalization
performance of deep neural networks. Yet, despite often facing limited data availability when …

Ultrasound is widely used in clinical practice due to its affordability, portability, and safety.
However, current AI research often overlooks combined disease prediction and tissue …

Ultrasound imaging, as a portable and radiation-free modality, presents challenges for
accurate segmentation due to the variability of lesions and the similar intensity values of …

Accurate segmentation of medical ultrasound images is crucial for guiding treatment
decisions and assessing intervention effectiveness. The challenge of segmenting lesions in …