AIMC Topic: Staining and Labeling

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Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.

JAMA network open May 1, 2020
IMPORTANCE: Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue sa...
Humans Prostatic Neoplasms Biopsy, Large-Core Needle Eosine Yellowish-(YS) Hematoxylin Aged Male Deep Learning Staining and Labeling
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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 Apr 1, 2020
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...
SOXE Transcription Factors Skin Neoplasms Staining and Labeling Immunohistochemistry Melanocytes Humans Neural Networks, Computer Machine Learning Algorithms Databases, Factual Melanoma
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Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

International journal of molecular sciences Mar 27, 2020
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection ...
Rare Diseases Humans Cell Tracking Blood Circulation Deep Learning Automation Staining and Labeling Neoplastic Cells, Circulating Hematologic Diseases Diagnostic Imaging Fluorescent Dyes Magnetics Flow Cytometry Cell Separation Diagnostic Tests, Routine
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Cellular and Molecular Probing of Intact Human Organs.

Cell Feb 13, 2020
Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-age...
Humans Brain Female Swine Deep Learning Thyroid Gland Imaging, Three-Dimensional Aged, 80 and over Animals Male Middle Aged Limit of Detection Pancreas Optical Imaging Eye Mice Staining and Labeling Kidney
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Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

Clinical cancer research : an official journal of the American Association for Cancer Research Oct 21, 2019
PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural netwo...
Biopsy Neoplasm Recurrence, Local Image Processing, Computer-Assisted Staining and Labeling Follow-Up Studies Area Under Curve Middle Aged Disease Progression Deep Learning Melanoma Neural Networks, Computer Risk Factors Algorithms Aged Young Adult Humans Retrospective Studies Aged, 80 and over Adult Female Survival Rate Male
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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.

Medical image analysis Sep 18, 2019
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables th...
Humans Neural Networks, Computer Colorectal Neoplasms Staining and Labeling Adenocarcinoma Datasets as Topic Cell Nucleus Histological Techniques
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RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification.

Medical image analysis Aug 30, 2019
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging du...
Calibration Deep Learning Staining and Labeling Diagnosis, Differential Datasets as Topic Humans Stomach Neoplasms
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Medical image analysis Aug 21, 2019
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with...
Pathology, Clinical Eosine Yellowish-(YS) Hematoxylin Humans Neural Networks, Computer Staining and Labeling Unsupervised Machine Learning Color Datasets as Topic
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Unsupervised method for normalization of hematoxylin-eosin stain in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 14, 2019
Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential l...
Color Coloring Agents Datasets as Topic Histocytochemistry Image Processing, Computer-Assisted Staining and Labeling Unsupervised Machine Learning Eosine Yellowish-(YS) Humans Hematoxylin Neoplasms
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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Leukocytes Staining and Labeling Humans Image Interpretation, Computer-Assisted Diagnosis, Differential Cell Lineage Machine Learning Predictive Value of Tests Pattern Recognition, Automated Leukemia Reproducibility of Results Acute Disease Blood Specimen Collection
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