Context-aware learning for cancer cell nucleus recognition in pathology images
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac167
Abstract: MOTIVATION Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for nucleus subtyping.… read more here.
Keywords: information; cell; pathology images; pathology ... See more keywords
Glomerular Lesion Recognition Based on Pathology Images With Annotation Noise via Noisy Label Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3269792
Abstract: Background: Glomerular lesion recognition is one of the most crucial steps in the diagnosis of kidney disease. Deep learning, which relies on large numbers of pathology images, assists pathologists to access glomerular lesions more efficiently,… read more here.
Keywords: annotation noise; pathology images; pathology; lesion recognition ... See more keywords
Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2015.2503720
Abstract: In digital pathology, to address color variation and histological component colocalization in pathology images, stain decomposition is usually performed preceding spectral normalization and tissue component segmentation. This paper examines the problem of stain decomposition, which… read more here.
Keywords: color; stain; pathology; stain decomposition ... See more keywords
Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer
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DOI: 10.1158/0008-5472.can-19-1629
Abstract: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression. The spatial… read more here.
Keywords: pathology; spatial organization; tumor; cell ... See more keywords
Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images
Sign Up to like & getrecommendations! Published in 2021 at "Cancer Management and Research"
DOI: 10.2147/cmar.s312608
Abstract: Introduction Breast cancer, one of the most common health threats to females worldwide, has always been a crucial topic in the medical field. With the rapid development of digital pathology, many scholars have used AI-based… read more here.
Keywords: pathology; class classification; multi class; classification ... See more keywords
Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2022.994950
Abstract: Prostate cancer can be diagnosed by prostate biopsy using transectal ultrasound guidance. The high number of pathology images from biopsy tissues is a burden on pathologists, and analysis is subjective and susceptible to inter-rater variability.… read more here.
Keywords: network; pathology images; prostate; pathology ... See more keywords
Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma
Sign Up to like & getrecommendations! Published in 2022 at "Genes"
DOI: 10.3390/genes13101916
Abstract: Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the… read more here.
Keywords: lncrnas protein; pathology images; melanoma; protein coding ... See more keywords