Medical Ultrasound (US), despite its wide use, is characterized by artefacts and operator
dependency. Those attributes hinder the gathering and utilization of US datasets for the …

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 …

Purpose Precise localization of cystic bone lesions is crucial for osteolytic bone tumor
surgery. Recently, there is a move toward ultrasound imaging over plain radiographs (X …

To provide a safe alternative, for intra-operative fluoroscopy, ultrasound (US) has been
investigated as an alternative safe imaging modality for various computer assisted …

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 learning (DL) methods typically require large datasets to effectively learn data
distributions. However, in the medical field, data is often limited in quantity, and acquiring …

Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several
millimeters in thickness have hindered the success of ultrasound (US) guided computer …

Anatomical segmentation of organs in ultrasound images is essential to many clinical
applications, particularly for diagnosis and monitoring. Existing deep neural networks …

Diagnostic imaging plays a critical role in healthcare, serving as a fundamental asset for
timely diagnosis, disease staging, and management as well as for treatment choice …

Techniques for data augmentation are widely employed to avoid overfitting, improve
generalizability and overcome data scarcity. This data-oriented approach frequently uses …