Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …

Objective Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-
the-art results on clinical natural language processing (NLP) tasks. One of the core …

Objective Clinical documents made available for secondary use play an increasingly
important role in discovery of clinical knowledge, development of research methods, and …

Sifting through vast textual data and summarizing key information from electronic health
records (EHR) imposes a substantial burden on how clinicians allocate their time. Although …

Neural image-to-text radiology report generation systems offer the potential to improve
radiology reporting by reducing the repetitive process of report drafting and identifying …

Transformers-based models, such as BERT, have dramatically improved the performance
for various natural language processing tasks. The clinical knowledge enriched model …

Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

Abstract Echinobase (www. echinobase. org) is a third generation web resource supporting
genomic research on echinoderms. The new version was built by cloning the mature …

Medical images are valuable for clinical diagnosis and decision making. Image modality is
an important primary step, as it is capable of aiding clinicians to access required medical …