Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

Detecting anatomical landmarks in medical images plays an essential role in understanding
the anatomy and planning automated processing. In recent years, a variety of deep neural …

Training saliency detection models with weak supervisions, eg, image-level tags or captions,
is appealing as it removes the costly demand of per-pixel annotations. Despite the rapid …

The success of deep learning methodologies hinges upon the availability of meticulously
labeled extensive datasets. However, when dealing with medical images, the annotation …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

The success of deep learning methods relies on the availability of a large number of
datasets with annotations; however, curating such datasets is burdensome, especially for …

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its
effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which …

The success of deep learning methods relies on the availability of well-labeled large-scale
datasets. However, for medical images, annotating such abundant training data often …

One-shot medical landmark detection gains much attention and achieves great success for
its label-efficient training process. However, existing one-shot learning methods are highly …

Accurate localization of anatomical landmarks has a critical role in clinical diagnosis,
treatment planning, and research. Most existing deep learning methods for anatomical …