AIMC Topic: Staining and Labeling

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Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin-eosin-stained tissue slides.

Histopathology Jul 1, 2024
AIMS: Uveal melanoma has a high propensity to metastasize. Prognosis is associated with specific driver mutations and copy number variations, and these can only be obtained after genetic testing. In this study we evaluated the efficacy of patient out...
Melanoma Aged Aged, 80 and over Adult Male Middle Aged Prognosis Retrospective Studies Female Humans Phosphoproteins Tumor Suppressor Proteins Eosine Yellowish-(YS) Deep Learning Staining and Labeling Mutation Ubiquitin Thiolesterase Uveal Melanoma Eukaryotic Initiation Factor-1 Hematoxylin RNA Splicing Factors Uveal Neoplasms
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Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.

Frontiers in immunology Jun 28, 2024
INTRODUCTION: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and person...
Deep Learning Lung Neoplasms Staining and Labeling Immunophenotyping Humans Biomarkers, Tumor Female T-Lymphocytes Male
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Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

Archives of microbiology Jun 21, 2024
In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating una...
Phenazines Gentian Violet Ethanol Unsupervised Machine Learning Image Processing, Computer-Assisted Staining and Labeling Bacteria
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AI-based computational H&E staining in lymphomas.

Journal of hematopathology Jun 13, 2024
Artificial Intelligence Staining and Labeling Lymphoma Eosine Yellowish-(YS) Hematoxylin Humans
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Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

International journal of computer assisted radiology and surgery Jun 7, 2024
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficul...
Unsupervised Machine Learning Kidney Glomerulus Coloring Agents Deep Learning Image Processing, Computer-Assisted Staining and Labeling Image Interpretation, Computer-Assisted Humans
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Using machine learning for chemical-free histological tissue staining.

Journal of histotechnology Apr 22, 2024
Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' ...
Liver Machine Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Staining and Labeling
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RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge.

Computer methods and programs in biomedicine Apr 22, 2024
BACKGROUND AND OBJECTIVE: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful t...
Deep Learning Image Processing, Computer-Assisted Staining and Labeling Algorithms Female Breast Neoplasms Image Interpretation, Computer-Assisted Humans Software
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Combining Artificial Intelligence and Simplified Image Processing for the Automatic Detection of Mycobacterium tuberculosis in Acid-fast Stain : A Cross-institute Training and Validation Study.

The American journal of surgical pathology Apr 9, 2024
Tuberculosis (TB) poses a significant health threat in Taiwan, necessitating efficient detection methods. Traditional screening for acid-fast positive bacilli in acid-fast stain is time-consuming and prone to human error due to staining artifacts. To...
Staining and Labeling Taiwan Predictive Value of Tests Tuberculosis Mycobacterium tuberculosis Image Interpretation, Computer-Assisted Automation, Laboratory Reproducibility of Results Deep Learning Humans Artificial Intelligence Bacteriological Techniques
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Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Apr 7, 2024
Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained ti...
Lung Neoplasms Staining and Labeling Supervised Machine Learning Humans Deep Learning Image Interpretation, Computer-Assisted Adenocarcinoma of Lung
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Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Scientific data Apr 3, 2024
Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address...
Histological Techniques Histology Eosine Yellowish-(YS) Machine Learning Image Processing, Computer-Assisted Staining and Labeling Humans Neural Networks, Computer
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