AIMC Topic: Immunohistochemistry

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A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue co...

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to ass...

A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.

European journal of radiology
PURPOSE: The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemical...

Computational analysis of morphological and molecular features in gastric cancer tissues.

Cancer medicine
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies...

Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided ...

A modular cGAN classification framework: Application to colorectal tumor detection.

Scientific reports
Automatic identification of tissue structures in the analysis of digital tissue biopsies remains an ongoing problem in digital pathology. Common barriers include lack of reliable ground truth due to inter- and intra- reader variability, class imbalan...

Deep-UV excitation fluorescence microscopy for detection of lymph node metastasis using deep neural network.

Scientific reports
Deep-UV (DUV) excitation fluorescence microscopy has potential to provide rapid diagnosis with simple technique comparing to conventional histopathology based on hematoxylin and eosin (H&E) staining. We established a fluorescent staining protocol for...

Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics.

AJR. American journal of roentgenology
The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with ...