AIMC Topic: Pathology

Clear Filters Showing 41 to 50 of 113 articles

Quick Annotator: an open-source digital pathology based rapid image annotation tool.

The journal of pathology. Clinical research
Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest...

Ethics of AI in Pathology: Current Paradigms and Emerging Issues.

The American journal of pathology
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured...

Applications of machine learning in the chemical pathology laboratory.

Journal of clinical pathology
Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly...

Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer.

Journal of gastroenterology
BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for T...

Generative Deep Learning in Digital Pathology Workflows.

The American journal of pathology
Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern com...

Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology.

Cells
Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Art...

Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?

The American journal of pathology
One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imagin...

Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.

Toxicologic pathology
The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxic...