AIMC Topic: Cell Nucleus

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HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.

BMC medical imaging Jan 7, 2025
PURPOSE: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital patholog...
Eosine Yellowish-(YS) Hematoxylin Image Processing, Computer-Assisted Cell Nucleus Humans Neural Networks, Computer
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Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Nature communications Dec 30, 2024
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional conf...
Infrared Rays Photoacoustic Techniques Microscopy, Confocal Deep Learning Fibroblasts Image Processing, Computer-Assisted Microscopy Unsupervised Machine Learning Humans Cell Nucleus
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Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model.

Scientific reports Dec 19, 2024
Image processing and pattern recognition methods have recently been extensively implemented in histopathological images (HIs). These computer-aided techniques are aimed at detecting the attentive biological markers for assisting the final cancer grad...
Neoplasms Deep Learning Cell Nucleus Mitosis Image Processing, Computer-Assisted Humans Algorithms
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Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems.

Computers in biology and medicine Dec 17, 2024
In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell sy...
Deep Learning Imaging, Three-Dimensional Humans Algorithms Spheroids, Cellular Image Processing, Computer-Assisted Cell Nucleus
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Comparison between two artificial intelligence models to discriminate cancerous cell nuclei based on confocal fluorescence imaging in hepatocellular carcinoma.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver Dec 13, 2024
BACKGROUND: Hepatocellular carcinoma (HCC) exhibits an exceptional intratumoral heterogeneity that might influence diagnosis and outcome. Advances in digital microscopy and artificial intelligence (AI) may improve the HCC identification of liver canc...
Humans Artificial Intelligence Carcinoma, Hepatocellular Microscopy, Confocal Cell Nucleus Deep Learning Machine Learning Liver Neoplasms Algorithms
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AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images.

Sensors (Basel, Switzerland) Nov 11, 2024
The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant chal...
Neural Networks, Computer Algorithms Deep Learning Humans Image Processing, Computer-Assisted Colorectal Neoplasms Cell Nucleus
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A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Medical image analysis Oct 5, 2024
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haemat...
Humans Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Cell Nucleus Neural Networks, Computer Algorithms
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Multifunctional aggregation network of cell nuclei segmentation aiming histopathological diagnosis assistance: A new MA-Net construction.

PloS one Sep 6, 2024
Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-aided diagnosis systems for histopathology. However, achieving accurate nuclear segmentation is chal...
Deep Learning Algorithms Humans Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Cell Nucleus Neural Networks, Computer
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Structure Embedded Nucleus Classification for Histopathology Images.

IEEE transactions on medical imaging Sep 3, 2024
Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected ...
Histocytochemistry Image Interpretation, Computer-Assisted Cell Nucleus Neural Networks, Computer Humans Algorithms Image Processing, Computer-Assisted
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UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

Communications biology Aug 30, 2024
Multiplexed imaging technologies have made it possible to interrogate complex tissue microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily an...
Unsupervised Machine Learning Bayes Theorem Deep Learning Humans Cell Nucleus Image Processing, Computer-Assisted Algorithms
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