Human-level blood cell counting on lens-free shadow images exploiting deep neural networks

The Analyst ◽  
2018 ◽  
Vol 143 (22) ◽  
pp. 5380-5387 ◽  
Author(s):  
DaeHan Ahn ◽  
JiYeong Lee ◽  
SangJun Moon ◽  
Taejoon Park
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Deep Neural Networks ◽  
Cell Counting ◽  
Blood Cell Counting ◽  
The Way

In-line holographic microscopes paved the way for realizing portable cell counting systems using deep neural networks.

scholarly journals Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

Sensors ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 1836 ◽  
Author(s):  
Xiwei Huang ◽  
Yu Jiang ◽  
Xu Liu ◽  
Hang Xu ◽  
Zhi Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Cell ◽  
Super Resolution ◽  
Cell Counting ◽  
Single Frame ◽  
Blood Cell Counting

Real-time Red Blood Cell Counting and Osmolarity Analysis using a Photoacoustic-based Microfluidic System

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Wenxiu Zhao ◽  
Haibo Yu ◽  
Yangdong Wen ◽  
Hao Luo ◽  
Boliang Jia ◽  
...  
Keyword(s):  
Blood Cell ◽  
Physical Properties ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Diagnostic Procedure ◽  
Microfluidic System ◽  
Cell Counting ◽  
Blood Samples ◽  
System P ◽  
Blood Cell Counting

Counting the number of red blood cells (RBCs) in blood samples is a common clinical diagnostic procedure, but conventional methods are unable to provide the size and other physical properties...

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

2017 ◽  
Vol 19 (12) ◽  
pp. 124014 ◽  
Author(s):  
Xi Liu ◽  
Mei Zhou ◽  
Song Qiu ◽  
Li Sun ◽  
Hongying Liu ◽  
...  
Keyword(s):  
Blood Cell ◽  
Hyperspectral Imaging ◽  
Red Blood Cell ◽  
Cell Counting ◽  
Counting Method ◽  
Imaging Technology ◽  
Blood Cell Counting

Worshiping Computers

2019 ◽  
pp. 85-110
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Common Sense ◽  
Deep Neural Networks ◽  
Computer Software ◽  
Square Roots ◽  
The World ◽  
The Way

Computer software, particularly deep neural networks and Monte Carlo simulations, are extremely useful for the specific tasks that they have been designed to do, and they will get even better, much better. However, we should not assume that computers are smarter than us just because they can tell us the first 2000 digits of pi or show us a street map of every city in the world. One of the paradoxical things about computers is that they can excel at things that humans consider difficult (like calculating square roots) while failing at things that humans consider easy (like recognizing stop signs). They can’t pass simple tests like the Winograd Schema Challenge because they do not understand the world the way humans do. They have neither common sense nor wisdom. They are our tools, not our masters.

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