scholarly journals Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8360
Author(s):  
Xiwei Huang ◽  
Hyungkook Jeon ◽  
Jixuan Liu ◽  
Jiangfan Yao ◽  
Maoyu Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Label Free ◽  
State Monitoring ◽  
Immune State ◽  
Free Classification

The authors wish to make the following correction to their paper [...]

scholarly journals Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 512
Author(s):  
Xiwei Huang ◽  
Jixuan Liu ◽  
Jiangfan Yao ◽  
Maoyu Wei ◽  
Wentao Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Of Care ◽  
White Blood Cells ◽  
Fluorescent Labeling ◽  
Fine Tuning ◽  
Differential Count ◽  
Label Free ◽  
Automatic Instrument ◽  
Free Classification

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient’s immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

scholarly journals Towards automated cancer screening: Label-free classification of fixed cell samples using wavelength modulated Raman spectroscopy

2018 ◽  
Vol 11 (4) ◽  
pp. e201700244 ◽  
Author(s):  
Lana Woolford ◽  
Mingzhou Chen ◽  
Kishan Dholakia ◽  
C. Simon Herrington
Keyword(s):  
Raman Spectroscopy ◽  
Cancer Screening ◽  
Label Free ◽  
Fixed Cell ◽  
Free Classification

scholarly journals Label-free classification of colon cancer grading using infrared spectral histopathology

2016 ◽  
Vol 187 ◽  
pp. 105-118 ◽  
Author(s):  
C. Kuepper ◽  
F. Großerueschkamp ◽  
A. Kallenbach-Thieltges ◽  
A. Mosig ◽  
A. Tannapfel ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Label Free ◽  
Infrared Spectral ◽  
Poorly Differentiated ◽  
Well Differentiated ◽  
Free Classification ◽  
Cancer Grading ◽  
Spectral Histopathology ◽  
Good Agreement

In recent years spectral histopathology (SHP) has been established as a label-free method to identify cancer within tissue. Herein, this approach is extended. It is not only used to identify tumour tissue with a sensitivity of 94% and a specificity of 100%, but in addition the tumour grading is determined. Grading is a measure of how much the tumour cells differ from the healthy cells. The grading ranges from G1 (well-differentiated), to G2 (moderately differentiated), G3 (poorly differentiated) and in rare cases to G4 (anaplastic). The grading is prognostic and is needed for the therapeutic decision of the clinician. The presented results show good agreement between the annotation by SHP and by pathologists. A correlation matrix is presented, and the results show that SHP provides prognostic values in colon cancer, which are obtained in a label-free and automated manner. It might become an important automated diagnostic tool at the bedside in precision medicine.

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