Improved accuracy in liver segmentation can lead to more accurate measurements of liver stiffness, which is a key biomarker for liver fibrosis.
The findings of the study indicate that the SimCLR+LR method exhibited high classification accuracy even with a limited number of labeled images.
Targeting these cells may therefore represent a promising strategy to treat and prevent AFib.
Molecular analysis of extracellular vesicles (EVs) in human bile samples could accurately detect patients with cholangiocarcinoma from patients with other benign or inflammatory conditions.
Researchers have developed and validated a deep learning–based method to detect Alzheimer’s disease based on routinely collected clinical brain images.
The COVID-19 pandemic introduced major challenges to the care of cancer patients, and imaging was one of the aspects that was severely impacted.
Mass General researchers detail the pathway to developing “zero error” ML systems that do not make unsafe prediction errors.
Investigators study changes in routine population health metrics may serve as early warnings of future crises.
The method can easily be repurposed for new problems using explainable artificial intelligence (xAI).
Clarivate Analytics’ Highly Cited Researchers List is an annual report of individuals at universities, research institutes and commercial organizations who demonstrate a significant and broad influence in their field or fields of research.
