Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis
Explore this paper's citation graph
Summary
A method to detect subtle changes in nuclear morphometrics at single-cell resolution by combining fluorescence imaging and deep learning is presented, which opens new avenues for early disease diagnostics and drug discovery.
- Type
- article
- Published
- 2017-12-01
- Cited by
- 42
- References
- 64
- Access
- Open access
- OpenAlex
- https://openalex.org/W2775341659
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:25752563
Keywords
Cancer, Cancer cell, Convolutional neural network, Nuclear imaging, Pipeline (software)
References
- Machine learning and computer vision approaches for phenotypic profiling
- Pathology of the Nucleus
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Appreciating force and shape — the rise of mechanotransduction in cell biology
- Development of a Nuclear Morphometric Signature for Prostate Cancer Risk in Negative Biopsies
- Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment
- Tumour cell invasion: an emerging role for basal epithelial cell extrusion
- Nuclear structure in cancer cells
- Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue
- Breaching the nuclear envelope in development and disease
- Microenvironmental regulation of tumor progression and metastasis
- Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach
- Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential
- Nuclear Mechanics and Mechanotransduction in Health and Disease
- Cancer diagnosis by nuclear morphometry using spatial information ,
- Microenvironmental regulation of metastasis
- Isotropic 3D Nuclear Morphometry of Normal, Fibrocystic and Malignant Breast Epithelial Cells Reveals New Structural Alterations
- The nuclear lamins: flexibility in function
- Future directions in cancer prevention.
Cited by
- Patchnet: Interpretable Neural Networks for Image Classification
- Nuclear Mechanopathology and Cancer Diagnosis.
- Chromatin’s physical properties shape the nucleus and its functions
- Nuclear morphometrics and chromatin condensation patterns as disease biomarkers using a mobile microscope
- The Application of Deep Learning in Cancer Prognosis Prediction
- Detecting breast cancer using artificial intelligence: Convolutional neural network
- Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms.
- Chromatin Rigidity Provides Mechanical and Genome Protection
- Modeling of Cell Nuclear Mechanics: Classes, Components, and Applications
- Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation
- Mechanical properties of single cells: Measurement methods and applications.
- Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): A novel architecture for accurate diagnosis/prognosis of Ovarian Cancer
- Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm
- Label-free microscopy of mitotic chromosomes using the polarization orthogonality breaking technique.
- Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL) For Accurate Diagnosis and Prognosis of Ovarian Cancer
- A review on machine learning approaches and trends in drug discovery
- Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm
- Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
- Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas
- A survey of physical methods for studying nuclear mechanics and mechanobiology
Related papers
- An Adaptable Real-Time Object Detection for Traffic Surveillance using R-CNN over CNN with Improved Accuracy
- SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS
- Convolutional Neural Network (CNN) Applied to the Risk Analysis of Accidents in Vessels Navigating the Amazon Rivers
- Age Estimation Method based on Comparative Convolutional Neural Network using Inception Module
- Convolutional Neural Network for Automated Analyzing of Medical Images
- Image Classification Using Convolutional Neural Network
- Real-time categorization of driver's gaze zone and head pose using the convolutional neural network
- Deep Convolution Neural Network for RBC Images
- Feasibility of Application of Repaired Pipeline on Surface Gathering Pipeline