Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

Background Clinicians consider both imaging and nonimaging data when diagnosing
diseases; however, current machine learning approaches primarily consider data from a …

Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …

Background Deep learning (DL) models can potentially improve prognostication of rectal
cancer but have not been systematically assessed. Purpose To develop and validate an MRI …

Background Deep learning is a promising way to improve health care. Image-processing
medical disciplines, such as pathology, are expected to be transformed by deep learning …

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and
has become a cornerstone of modern precision oncology. Histopathological evaluation is …

The survival analysis on histological whole-slide images (WSIs) is one of the most important
means to estimate patient prognosis. Although many weakly-supervised deep learning …

Bioprinting is a versatile technology which is gaining rapid adoption in healthcare fields
such as tissue engineering, regenerative medicine, drug delivery, and surgical planning …