Background Advances in self-supervised learning (SSL) have enabled state-of-the-art
automated medical image diagnosis from small, labeled datasets. This label efficiency is …

Contrastive learning has proven useful in many applications where access to labelled data
is limited. The lack of annotated data is particularly problematic in medical image …

Current convolutional neural network-based ultrasound automatic classification models for
prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning …

Precise and rapid categorization of images in the B-scan ultrasound modality is vital for
diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still …

The accurate recognition of anatomical structures in fetal ultrasound images is crucial for
prenatal diagnosis and determining ultrasound standard planes. However, this task can be …

Deep learning models trained with an insufficient volume of data can often fail to generalize
between different equipment, clinics, and clinicians or fail to achieve acceptable …

Transthoracic Echocardiography (TTE) is the most widely-used screening method for the
detection of pulmonary hypertension (PH), a life-threatening cardiopulmonary disorder that …

Supervised training of deep learning models for medical imaging applications requires a
significant amount of labeled data. This is posing a challenge as the images are required to …

Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on
medical imaging data can provide a strong initialization for downstream supervised …

Contrastive learning has proven useful in many applications where access to labelled data
is limited. The lack of annotated data is particularly problematic in medical image segmenta …