AIMC Topic: Pathologists

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A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-a...

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Virchows Archiv : an international journal of pathology
Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial i...

DeepTree: Pathological Image Classification Through Imitating Tree-Like Strategies of Pathologists.

IEEE transactions on medical imaging
Digitization of pathological slides has promoted the research of computer-aided diagnosis, in which artificial intelligence analysis of pathological images deserves attention. Appropriate deep learning techniques in natural images have been extended ...

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

Scientific reports
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
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
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
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
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
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
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...