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Cell Nucleus

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Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images.

IEEE transactions on medical imaging
Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce en...

Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.

Methods (San Diego, Calif.)
Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maint...

Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Robust and accurate nuclei localization in microscopy image can provide crucial clues for accurate computer-aid diagnosis. In this paper, we propose a convolutional neural network (CNN) based hough voting method to localize nucleus centroids with hea...

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

Scientific reports
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster sca...

An Automatic Learning-Based Framework for Robust Nucleus Segmentation.

IEEE transactions on medical imaging
Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus...

Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis.

IEEE journal of biomedical and health informatics
In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to...

Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving ...

Does artificial intelligence redefine nuclear-to-cytoplasmic ratio threshold for diagnosing high-grade urothelial carcinoma?

Cancer cytopathology
BACKGROUND: The Paris System (TPS) introduced standardized nuclear-to-cytoplasmic (N/C) ratio thresholds for urine cytology to improve high-grade urothelial carcinoma (HGUC) detection, but these criteria remain subjective. This study used AIxURO, an ...

Deep generative model for protein subcellular localization prediction.

Briefings in bioinformatics
Protein sequence not only determines its structure but also provides important clues of its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them pro...