AIMC Topic: Immunohistochemistry
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Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.
Pathology, research and practice
Jun 1, 2025
CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.
Self-HER2Net: A generative self-supervised framework for HER2 classification in IHC histopathology of breast cancer.
Pathology, research and practice
Jun 1, 2025
Breast cancer is a significant global health concern, where precise identification of proteins like Human Epidermal Growth Factor Receptor 2 (HER2) in cancer cells via Immunohistochemistry (IHC) is pivotal for treatment decisions. HER2 overexpression...
Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions.
Acta histochemica
Jun 1, 2025
Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in the endometrial corpus is crucial for patient prognosis and early treatment, although their distinctio...
An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.
Scientific reports
May 31, 2025
Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus m...
Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.
The journal of pathology. Clinical research
Mar 1, 2025
Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody-drug conjugates (ADCs) like trastuzumab deruxtecan, which shows pr...
Artificial intelligence-based tissue segmentation and cell identification in multiplex-stained histological endometriosis sections.
Human reproduction (Oxford, England)
Mar 1, 2025
STUDY QUESTION: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
A machine learning approach to predict HPV positivity of oropharyngeal squamous cell carcinoma.
Pathologica
Dec 1, 2024
HPV status is an important prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC), with HPV-positive tumors associated with better overall survival. To determine HPV status, we rely on the immunohistochemical investigation for expression ...
Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology.
Clinical cancer research : an official journal of the American Association for Cancer Research
Nov 15, 2024
PURPOSE: The spatial variability and clinical relevance of the tumor immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). In this study, we aim to develop a deep learning (DL)-based image analysis model for t...
GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images.
Bioinformatics (Oxford, England)
Oct 31, 2022
MOTIVATION: Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable v...
Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images.
Bioinformatics (Oxford, England)
Apr 28, 2022
MOTIVATION: The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction...
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