Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …

Deep learning methods are widely applied in digital pathology to address clinical
challenges such as prognosis and diagnosis. As one of the most recent applications, deep …

Simple Summary Breast cancer diagnosis and treatment can be improved by understanding
the specific genetic makeup of a patient's tumour. Currently, this genetic information is …

Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …

More humans have died of tuberculosis (TB) than any other infectious disease and millions
still die each year. Experts advocate for blood-based, serum protein biomarkers to help …

Molecular and genomic properties are critical in selecting cancer treatments to target
individual tumors, particularly for immunotherapy. However, the methods to assess such …

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually
in the United States alone. Clinicians often rely on the breast cancer recurrence score …

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8–9
million new tuberculosis (TB) cases and 1–1.5 million deaths each year. Most patients have …

Background P4 medicine (predict, prevent, personalize, and participate) is a new approach
to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and …