AIMC Topic: Staining and Labeling

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Rethinking ImageNet Pre-training for Computational Histopathology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on ...
Staining and Labeling Coloring Agents Neural Networks, Computer
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A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of ...
Staining and Labeling Cell Communication Neural Networks, Computer Tumor Microenvironment
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Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.

World journal of gastroenterology Nov 28, 2021
BACKGROUND: Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutation...
Genes, p53 Stomach Neoplasms Deep Learning Staining and Labeling Mutation Humans
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A Deep Learning-Based Approach for Glomeruli Instance Segmentation from Multistained Renal Biopsy Pathologic Images.

The American journal of pathology Aug 1, 2021
Glomeruli instance segmentation from pathologic images is a fundamental step in the automatic analysis of renal biopsies. Glomerular histologic manifestations vary widely among diseases and cases, and several special staining methods are necessary fo...
Staining and Labeling Kidney Diseases Humans Deep Learning Image Interpretation, Computer-Assisted Biopsy Kidney Glomerulus
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Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.

The Lancet. Oncology Jan 1, 2021
BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients...
Eosine Yellowish-(YS) Hematoxylin Microscopy Image Interpretation, Computer-Assisted Staining and Labeling Predictive Value of Tests Microsatellite Instability Coloring Agents Deep Learning Genetic Predisposition to Disease Colorectal Neoplasms Diagnosis, Computer-Assisted Humans Phenotype Reproducibility of Results
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Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue.

Molecular imaging and biology Oct 1, 2020
PURPOSE: Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. However, the variable and labor-intensive tissue staining procedures hinder efficient and informative histolo...
Neural Networks, Computer Animals Male Carotid Arteries Rats, Sprague-Dawley Deep Learning Microscopy Staining and Labeling
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Classification of Histologic Images Using a Single Staining: Experiments with Deep Learning on Deconvolved Images.

Studies in health technology and informatics Jun 16, 2020
The automated analysis of digitized immunohistochemistry microscope slides is usually a challenging task, because markers should be analysed on the tumor area only. Tumor areas could be recognized on a different slide, stained with Haematoxylin-Eosin...
Image Interpretation, Computer-Assisted Humans Deep Learning Prospective Studies Staining and Labeling Neoplasms
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Artificial Intelligence Will Not Replace Health Professionals, but the Proper Use of Artificial Intelligence Will Make Health Professionals Better.

Cancer research May 15, 2020
Deep learning has enabled great advances to be made in cancer research with regards to diagnosis, prognosis, and treatment. The study by Wang and colleagues in this issue of develops a deep learning algorithm with the ability to digitally stain hist...
Artificial Intelligence Algorithms Humans Staining and Labeling Tumor Microenvironment Lung Neoplasms
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Computational staining of unlabelled tissue.

Nature biomedical engineering Jun 1, 2019
Staining and Labeling Deep Learning
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Colorization of H&E stained tissue using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Histopathology is a critical tool in the diagnosis and stratification of cancer. Digital Pathology involves the scanning of stained and fixed tissue samples to produce highresolution images that can be used for computer-aided diagnosis and research. ...
Image Processing, Computer-Assisted Diagnosis, Computer-Assisted Color Humans Staining and Labeling Deep Learning Neoplasms
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