scholarly journals Cell Classification for Layout Recognition in Spreadsheets

2018 ◽  
pp. 78-100
Author(s):  
Elvis Koci ◽  
Maik Thiele ◽  
Oscar Romero ◽  
Wolfgang Lehner
Keyword(s):  
Cell Classification

Use of Velocity Vectors for Cell Classification Under Acoustic Drifting Forces

2020 ◽  
Author(s):  
Rahmetullah Varol ◽  
Sevde Omeroglu ◽  
Zeynep Karavelioglu ◽  
Ela Kumuk ◽  
Eda Nur Saruhan ◽  
...  
Keyword(s):  
Cell Classification

Stochastic Models for Multistage Cell Classification Systems

1978 ◽  
Vol BME-25 (4) ◽  
pp. 368-373
Author(s):  
James L. Cambier ◽  
Leon L. Wheeless
Keyword(s):  
Stochastic Models ◽  
Classification Systems ◽  
Cell Classification

scholarly journals Learning deep features for dead and living breast cancer cell classification without staining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gisela Pattarone ◽  
Laura Acion ◽  
Marina Simian ◽  
Emmanuel Iarussi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Drug Treatment ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Biology ◽  
Breast Cancer Cells ◽  
Bright Field ◽  
Cell Classification

AbstractAutomated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.

Determination of Updated Fatigue Properties of PE 4710 Cell Classification 445574C High Density Polyethylene

2014 ◽  
Author(s):  
Timothy M. Adams ◽  
Shawn Nickholds ◽  
Douglas Munson ◽  
Jeffery Andrasik
Keyword(s):  
Carbon Steel ◽  
Pressure Vessel ◽  
High Density Polyethylene ◽  
Nuclear Power ◽  
Fatigue Testing ◽  
High Density ◽  
Labor Costs ◽  
Cell Classification ◽  
Service Water ◽  
Hdpe Pipe

For corroded piping in low temperature systems, such as service water systems in nuclear power plants, replacement of carbon steel piping with high density polyethylene (HDPE) is a cost-effective solution. Polyethylene pipe can be installed at much lower labor costs that carbon steel pipe and HDPE pipe has a much greater resistance to corrosion. The ASME Boiler and Pressure Vessel Code, Section III, Division 1 currently permits the use of non-metallic piping in buried safety Class 3 piping systems. Additionally, HDPE pipe has been successfully used in non-safety-related systems in nuclear power facilities and is commonly used in other industries such as water mains and natural gas pipelines. This report presents the results of updated fatigue testing of PE 4710 cell classification 445574C pipe compliant with the specific Code requirements. This information was developed to support and provide a strong technical basis for material properties of HDPE pipe for use in ASME Boiler and Pressure Vessel Code, Section III New Construction and Section XI repair or replacement activities. The data may also be useful for applications of HDPE pipe in commercial electric power generation facilities and chemical, process and waste water plants via its possible use in the B31 series piping codes. The report provides fatigue data in the form of Code S-N curves for fusion butt joints in PE 4710 cell classification 445574C HDPE pipe.

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