Search Results for author:
Found 9 papers, 1 papers with code
Evaluating GPT's Programming Capability through CodeWars' Katas
In the burgeoning field of artificial intelligence (AI), understanding the capabilities and limitations of programming-oriented models is crucial.
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging
Training deep learning models on cardiac magnetic resonance imaging (CMR) can be a challenge due to the small amount of expert generated labels and inherent complexity of data source.
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification
Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them.
Estimation of Clinical Workload and Patient Activity using Deep Learning and Optical Flow
Contactless monitoring using thermal imaging has become increasingly proposed to monitor patient deterioration in hospital, most recently to detect fevers and infections during the COVID-19 pandemic.
Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR
In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text.
