AIMC Topic: Staining and Labeling

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Stain-free detection of embryo polarization using deep learning.

Scientific reports Feb 14, 2022
Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, im...
Deep Learning Mice Staining and Labeling Fertilization in Vitro Humans Animals Coloring Agents Embryo, Mammalian Embryonic Development Cell Polarity
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Subtype classification of malignant lymphoma using immunohistochemical staining pattern.

International journal of computer assisted radiology and surgery Feb 11, 2022
PURPOSE: For the image classification problem, the construction of appropriate training data is important for improving the generalization ability of the classifier in particular when the size of the training data is small. We propose a method that q...
Humans Neural Networks, Computer Staining and Labeling Lymphoma
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Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.

Cancer research Dec 1, 2021
Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dim...
Biopsy, Large-Core Needle Deep Learning Cohort Studies Imaging, Three-Dimensional Staining and Labeling Prostatic Neoplasms Prostate Prostatectomy Aged Male Middle Aged Humans Risk Assessment
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The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The Journal of pathology Nov 6, 2021
The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to re...
Algorithms Neoplasms Staining and Labeling United Kingdom Humans Artificial Intelligence Eosine Yellowish-(YS) Hematoxylin
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Automated annotations of epithelial cells and stroma in hematoxylin-eosin-stained whole-slide images using cytokeratin re-staining.

The journal of pathology. Clinical research Oct 30, 2021
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape...
Eosine Yellowish-(YS) Hematoxylin Humans Artificial Intelligence Deep Learning Staining and Labeling Epithelial Cells Keratins
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Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks.

The British journal of ophthalmology Oct 20, 2021
PURPOSE: The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks.
Neural Networks, Computer Cornea Image Processing, Computer-Assisted Staining and Labeling Humans Fluorescein
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Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Tissue & cell Sep 29, 2021
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei an...
Staining and Labeling Melanoma, Cutaneous Malignant Cell Nucleus Eosine Yellowish-(YS) Melanoma Hematoxylin Humans Skin Neoplasms Neural Networks, Computer Deep Learning Algorithms Image Processing, Computer-Assisted
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Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

The Journal of pathology Sep 14, 2021
Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although expert knowledge is reliable for annotating regions informative of malignancy and other known histolo...
Proto-Oncogene Proteins B-raf Middle Aged Thyroid Neoplasms Staining and Labeling Mutation Aged Adult Male Humans Neural Networks, Computer Female
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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Scientific reports Aug 16, 2021
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires ...
Datasets as Topic Deep Learning Smoking Humans ROC Curve Adenocarcinoma of Lung Aged, 80 and over Staining and Labeling Female Lung Neoplasms Eosine Yellowish-(YS) Adult Aged Carcinoma, Non-Small-Cell Lung Hematoxylin Male Age Factors Middle Aged Area Under Curve Mutation Coloring Agents Sex Factors
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Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Arteriosclerosis, thrombosis, and vascular biology Aug 12, 2021
Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that inc...
Atherosclerosis Microscopy Image Interpretation, Computer-Assisted Vascular Remodeling Mice, Knockout, ApoE Plaque, Atherosclerotic Reproducibility of Results Arteries Animals Staining and Labeling Male Software Disease Models, Animal Diagnosis, Computer-Assisted Severity of Illness Index Female Mice, Inbred C57BL Deep Learning Predictive Value of Tests Mice
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