AIMC Topic: Staining and Labeling

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BACH: Grand challenge on breast cancer histology images.

Medical image analysis May 31, 2019
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysi...
Pattern Recognition, Automated Breast Neoplasms Staining and Labeling Microscopy Humans Neural Networks, Computer Algorithms Female
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Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection.

Cells May 23, 2019
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are common...
Humans Deep Learning Image Processing, Computer-Assisted Cell Nucleus Computer Storage Devices Eosine Yellowish-(YS) Hematoxylin Staining and Labeling Logistic Models Microscopy, Fluorescence Models, Biological
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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

Nature biomedical engineering Mar 4, 2019
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wi...
Staining and Labeling Liver Reference Standards Melanins Lung Humans Fluorescence Neural Networks, Computer Deep Learning Algorithms Image Processing, Computer-Assisted
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High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

IEEE transactions on bio-medical engineering Feb 13, 2019
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...
Humans Machine Learning Automation, Laboratory Staining and Labeling Coloring Agents Immunohistochemistry Microfluidic Analytical Techniques Female Sensitivity and Specificity Breast Neoplasms Biomarkers, Tumor
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Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach.

Medical image analysis Jul 12, 2018
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...
Immunohistochemistry Color Image Interpretation, Computer-Assisted Automation Humans Colorectal Neoplasms Biomarkers, Tumor Deep Learning Staining and Labeling
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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BMC cancer May 30, 2018
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...
Cell Nucleus Receptors, Estrogen Genetic Testing Breast Models, Biological Supervised Machine Learning Predictive Value of Tests Principal Component Analysis Neoplasm Staging Aged Young Adult Adult Breast Neoplasms Middle Aged ROC Curve Image Processing, Computer-Assisted Staining and Labeling Humans Prognosis Female Risk Factors
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MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.

Artificial intelligence in medicine Apr 26, 2018
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of ...
Eosine Yellowish-(YS) Hematoxylin Coloring Agents Microscopy Image Interpretation, Computer-Assisted Pattern Recognition, Automated Staining and Labeling Predictive Value of Tests Biopsy Female Humans Reproducibility of Results Databases, Factual Breast Neoplasms Deep Learning
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Automated Preparation of MS-Sensitive Fluorescently Labeled N-Glycans with a Commercial Pipetting Robot.

SLAS technology Apr 4, 2018
N-Glycan analysis is routinely performed for biotherapeutic protein characterization. A recently introduced N-glycan analysis kit using RapiFluor-MS (RFMS) labeling provides time savings over reductive amination labeling methods while also providing ...
Automation, Laboratory Reproducibility of Results Staining and Labeling Mass Spectrometry Proteins Robotics Fluorescent Dyes Polysaccharides
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Image analysis and machine learning for detecting malaria.

Translational research : the journal of laboratory and clinical medicine Jan 12, 2018
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts a...
Humans Machine Learning Smartphone Staining and Labeling Malaria Blood Sedimentation
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A novel peptide/FeO@SiO-Au nanocomposite-based fluorescence biosensor for the highly selective and sensitive detection of prostate-specific antigen.

Talanta Nov 21, 2017
Highly selective and sensitive detection methods are very important for the early diagnosis of prostate-specific antigen (PSA). Here, we present a novel peptide/FeO@SiO-Au nanocomposite-based fluorescence biosensor for highly selective and sensitive ...
Ferrosoferric Oxide Fluoresceins Fluorescence Recovery After Photobleaching Silicon Dioxide Peptides Magnetite Nanoparticles Humans Limit of Detection Fluorescent Dyes Biosensing Techniques Reproducibility of Results Prostate-Specific Antigen Protein Binding Prostate Male Staining and Labeling Nanocomposites
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