AIMC Topic: Pathologists
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Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.
Scientific reports
Mar 26, 2024
Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers h...
DiagSet: a dataset for prostate cancer histopathological image classification.
Scientific reports
Mar 21, 2024
Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted fro...
Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.
Scientific reports
Mar 4, 2024
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on...
Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation.
The American journal of pathology
Feb 5, 2024
Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides [ie, whole slide images (WSIs)]. While trained histopathologists are able to diagnose diseases by examining WSIs visually, this pr...
Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology.
Toxicologic pathology
Jan 30, 2024
Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color repr...
MAPS: pathologist-level cell type annotation from tissue images through machine learning.
Nature communications
Jan 2, 2024
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resour...
Machine learning and machine teaching in histopathology.
Pathology, research and practice
Dec 16, 2023
An artificial intelligence (AI) platform was trained by a consultant histopathologist to classify whole slide images (WSIs) of large bowel biopsies. Six medical students viewed WSIs of five large bowel biopsy cases and assigned the WSIs to one of the...
Artificial intelligence in the practice of forensic medicine: a scoping review.
International journal of legal medicine
Dec 13, 2023
Forensic medicine is a thriving application field for artificial intelligence (AI). Indeed, AI applications intended to forensic pathologists or forensic physicians have emerged since the last decade. For example, AI models were developed to help est...
A Fully Automated Artificial Intelligence System to Assist Pathologists' Diagnosis to Predict Histologically High-grade Urothelial Carcinoma from Digitized Urine Cytology Slides Using Deep Learning.
European urology oncology
Dec 7, 2023
BACKGROUND: Urine cytology, although a useful screening method for urothelial carcinoma, lacks sensitivity. As an emerging technology, artificial intelligence (AI) improved image analysis accuracy significantly.
Bridging bytes and biopsies: A comparative analysis of ChatGPT and histopathologists in pathology diagnosis and collaborative potential.
Histopathology
Nov 30, 2023
BACKGROUND AND AIMS: ChatGPT is a powerful artificial intelligence (AI) chatbot developed by the OpenAI research laboratory which is capable of analysing human input and generating human-like responses. Early research into the potential application o...
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