Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

In medical Vision-Language Pre-training (VLP), significant work focuses on extracting text
and image features from clinical reports and medical images. Yet, existing methods may …

The burgeoning integration of 3D medical imaging into healthcare has led to a substantial
increase in the workload of medical professionals. To assist clinicians in their diagnostic …

Recently, multi-modal vision-language foundation models have gained significant attention
in the medical field. While these models offer great opportunities, they still face crucial …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Electron microscopy (EM) images are notoriously challenging to segment due to their
complex structures and lack of effective annotations. Fortunately, large-scale self-supervised …

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical,
non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have …

Expert annotation of 3D medical image for downstream analysis is resource-intensive,
posing challenges in clinical applications. Visual self-supervised learning (vSSL), though …

The scarcity of annotated data has sparked significant interest in unsupervised pre-training
methods that leverage medical reports as auxiliary signals for medical visual representation …

Self-supervised learning (SSL) is an efficient pre-training method for medical image
analysis. However current research is mostly confined to certain modalities consuming …