AIMC Topic: Mitosis

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Machine learning techniques for mitoses classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Nov 27, 2020
BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, thei...
Female Breast Neoplasms Mitosis Humans Neural Networks, Computer Machine Learning
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Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net.

Scientific reports Feb 14, 2020
Assessing the structure and function of organelles in living organisms of the primitive unicellular red algae Cyanidioschyzon merolae on three-dimensional sequential images demands a reliable automated technique in the class imbalance among various c...
Mitosis Organelles Rhodophyta Image Processing, Computer-Assisted Microscopy, Electron, Scanning Neural Networks, Computer Algorithms Imaging, Three-Dimensional
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Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

IEEE journal of biomedical and health informatics Sep 23, 2019
The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided...
Neural Networks, Computer Cells, Cultured Mitosis Microscopy, Phase-Contrast Time-Lapse Imaging Image Processing, Computer-Assisted Animals Mice Deep Learning Stem Cells
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Cell mitosis event analysis in phase contrast microscopy images using deep learning.

Medical image analysis Jun 22, 2019
In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient ...
Deep Learning Mesenchymal Stem Cells Artifacts Neural Networks, Computer Cell Tracking Image Enhancement Mitosis Microscopy, Phase-Contrast
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Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

Computers in biology and medicine Jun 17, 2019
We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodies (ANA) detection. In the proposed work, due to unique characteristics (the rare occurrence) of the ...
Antibodies, Antinuclear ROC Curve Image Processing, Computer-Assisted Support Vector Machine Autoimmune Diseases Mitosis Humans Neural Networks, Computer Cell Line, Tumor
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Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.

Cellular oncology (Dordrecht, Netherlands) Apr 15, 2019
PURPOSE: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we a...
Female Multivariate Analysis Middle Aged Mitosis Deep Learning Triple Negative Breast Neoplasms Humans Disease-Free Survival Algorithms Proportional Hazards Models Prognosis
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Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.

IEEE transactions on medical imaging Mar 25, 2019
Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural netw...
Humans Neural Networks, Computer Algorithms Databases, Factual Image Processing, Computer-Assisted Cytological Techniques Microscopy Mitosis
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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Medical image analysis Feb 27, 2019
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor prolifera...
Gene Expression Mitosis Pathology Humans Cell Proliferation Predictive Value of Tests Biomarkers, Tumor Prognosis Deep Learning Female Image Processing, Computer-Assisted Breast Neoplasms
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Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches.

Computers in biology and medicine Nov 5, 2018
In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evalu...
Image Processing, Computer-Assisted Mitosis Female Breast Neoplasms Humans Neural Networks, Computer Algorithms
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DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Medical image analysis Jan 31, 2018
Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate...
Image Processing, Computer-Assisted Female Mitosis Breast Neoplasms Humans Deep Learning Algorithms
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