AIMC Topic: Immunohistochemistry

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AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage.

Scientific reports
In routine breast cancer diagnostics, pathologists often review each case twice-first to determine the need for immunohistochemical (IHC) stains, and a second time to issue the final diagnosis-creating significant workload and delays. We present an a...

Automatically quantifying spatial heterogeneity of immune and tumor hypoxia environment and predicting disease-free survival for patients with rectal cancer.

Cancer immunology, immunotherapy : CII
Immunohistochemistry (IHC) remains the gold standard for evaluating protein expression in tumor microenvironment analysis. This approach hinders robust correlation analyses between spatial heterogeneity in the tumor microenvironment and clinical outc...

Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.

Breast cancer research : BCR
BACKGROUND: Accurate quantification of the Ki-67 proliferation index is essential for breast cancer prognosis and treatment planning. Current automated methods, including classical and deep learning approaches based on cell detection or segmentation,...

IHC-DualNet: a dual-branch graph-based architecture for interpretable and precise immunohistochemistry tissue segmentation.

Physics in medicine and biology
Immunohistochemistry (IHC) is a cornerstone technique in oncology, where accurate tissue region segmentation is critical for diagnosis and prognosis. However, current clinical workflows rely heavily on manual annotation, which is time-consuming, subj...

A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.

BMC medical imaging
BACKGROUND: AI-based models for analysis of histopathology whole slide images (WSIs) are now common. However, image quality, particularly unsharp areas of WSIs, impacts model performance. In this study we investigate the impact of blur on deep learni...

Real-world data of CanAssist Breast- first immunohistochemistry and AI-based prognostic test.

Scientific reports
CanAssist Breast (CAB), an immunohistochemistry (IHC) and artificial intelligence-based prognostic test, was developed on Hormone receptor-positive (HR +), HER2/neu-negative (HER2-) breast tumors from Indian patients and validated in retrospective gl...

AI microscope facilitates accurate interpretation of HER2 immunohistochemical scores 0 and 1+ in invasive breast cancer.

Scientific reports
Accurate interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) scores 0 and 1+ is crucial for treating HER2-low breast cancer patients with antibody-drug conjugates. To improve diagnostic precision, we developed...

AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.

Diagnostic pathology
Presently, pathologists need to stain biopsy samples with standard and antibody-based immunocytochemistry (ICC) reagents for final diagnosis. Antibody reagents take hours to days to perform staining, along with requiring specialized equipment and tec...

Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides.

PloS one
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence. Automatic evaluation could improve diagnostic efficiency and find associations between morphological features and clinical outcome. For developm...