[HTML][HTML] Deep learning for chest X-ray analysis: A survey
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 …
image analysis tasks. As the most commonly performed radiological exam, chest …
[HTML][HTML] The importance of resource awareness in artificial intelligence for healthcare
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 …
range of healthcare applications, from medical image computing and analysis to continuous …
A comparative study of pretrained language models for long clinical text
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 …
the-art results on clinical natural language processing (NLP) tasks. One of the core …
Preparing a collection of radiology examinations for distribution and retrieval
D Demner-Fushman, MD Kohli… - Journal of the …, 2016 - academic.oup.com
Objective Clinical documents made available for secondary use play an increasingly
important role in discovery of clinical knowledge, development of research methods, and …
important role in discovery of clinical knowledge, development of research methods, and …
[HTML][HTML] Clinical text summarization: adapting large language models can outperform human experts
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 …
records (EHR) imposes a substantial burden on how clinicians allocate their time. Although …
Improving factual completeness and consistency of image-to-text radiology report generation
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 …
radiology reporting by reducing the repetitive process of report drafting and identifying …
Clinical-longformer and clinical-bigbird: Transformers for long clinical sequences
Transformers-based models, such as BERT, have dramatically improved the performance
for various natural language processing tasks. The clinical knowledge enriched model …
for various natural language processing tasks. The clinical knowledge enriched model …
[HTML][HTML] A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …
healthcare, enabling a comprehensive understanding of patient health and personalized …
Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms
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 …
genomic research on echinoderms. The new version was built by cloning the mature …
[HTML][HTML] Deep transfer learning for modality classification of medical images
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 …
an important primary step, as it is capable of aiding clinicians to access required medical …