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Staining and Labeling

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Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently int...

From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization.

Medicina (Kaunas, Lithuania)
This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accu...

PXPermute reveals staining importance in multichannel imaging flow cytometry.

Cell reports methods
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibo...

Virtual histological staining of unlabeled autopsy tissue.

Nature communications
Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial la...

Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system.

Diagnostic pathology
BACKGROUND: Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual sys...

Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images.

Computers in biology and medicine
Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension to make decisions based rather on bias in training dataset than on concrete biological features, thus...

Current Landscape of Advanced Imaging Tools for Pathology Diagnostics.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two...

Virtual staining for histology by deep learning.

Trends in biotechnology
In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replaceme...

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors.

Clinical and translational gastroenterology
INTRODUCTION: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising res...

Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning.

American journal of clinical pathology
OBJECTIVES: Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general pur...