AIMC Topic: Immunohistochemistry

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Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Trends in cancer
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most exten...

Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...

High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

IEEE transactions on bio-medical engineering
Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parame...

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Scientific reports
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classificat...

Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning.

eLife
The highly organized spatial arrangement of sensory hair cells in the organ of Corti is essential for inner ear function. Here, we report a new analytical pipeline, based on optical clearing of tissue, for the construction of a single-cell resolution...

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.

Scientific reports
The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression current...

Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes.

IEEE/ACM transactions on computational biology and bioinformatics
Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future ...

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

IEEE transactions on medical imaging
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul...

Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit.

Clinical cancer research : an official journal of the American Association for Cancer Research
Current tumor-node-metastasis (TNM) staging system cannot provide adequate information for prediction of prognosis and chemotherapeutic benefits. We constructed a classifier to predict prognosis and identify a subset of patients who can benefit from...

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach.

Medical image analysis
In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, includ...