Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

Simple Summary Large-scale medical data carries significant areas of underrepresentation
and bias at all levels: clinical, biological, and management. Resulting data sets and outcome …

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a
demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets …

Multimodal learning has gained immense popularity due to the explosive growth in the
volume of image and textual data in various domains. Vision-language heterogeneous …

Simple Summary Structure name standardization is a critical problem in Radiotherapy
planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes …

Many studies have proposed deep learning models to diagnose faults and predict accidents
in nuclear power reactors. However, the training data in these studies are deterministic, and …

The rapid growth of biomedical literature has necessitated the development of advanced
information extraction techniques to unlock valuable insights from unstructured text. Named …

Physicians often label anatomical structure sets in Digital Imaging and Communications in
Medicine (DICOM) images with nonstandard names. As these names vary widely, the …

Abstract Structure name standardization is a critical problem in Radiotherapy planning
systems to correctly identify the various Organs-at-Risk, Planning Target Volumes …