Towards quantification and differentiation of protein aggregates and silicone oil droplets in the low micrometer and submicrometer size range by using oil-immersion flow imaging microscopy and convolutional neural networks

2021 ◽  
Author(s):  
Muhammad Umar ◽  
Nils Krause ◽  
Andrea Hawe ◽  
Friedrich Simmel ◽  
Tim Menzen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Size Range ◽  
Silicone Oil ◽  
Protein Aggregates ◽  
Flow Imaging ◽  
Oil Droplets ◽  
Oil Immersion

Micro–Flow Imaging and Resonant Mass Measurement (Archimedes) – Complementary Methods to Quantitatively Differentiate Protein Particles and Silicone Oil Droplets

2013 ◽  
Vol 102 (7) ◽  
pp. 2152-2165 ◽  
Author(s):  
Daniel Weinbuch ◽  
Sarah Zölls ◽  
Michael Wiggenhorn ◽  
Wolfgang Friess ◽  
Gerhard Winter ◽  
...  
Keyword(s):  
Silicone Oil ◽  
Mass Measurement ◽  
Flow Imaging ◽  
Oil Droplets ◽  
Complementary Methods ◽  
Micro Flow ◽  
Protein Particles

Discrimination Between Silicone Oil Droplets and Protein Aggregates in Biopharmaceuticals: A Novel Multiparametric Image Filter for Sub-visible Particles in Microflow Imaging Analysis

2011 ◽  
Vol 29 (2) ◽  
pp. 594-602 ◽  
Author(s):  
René Strehl ◽  
Verena Rombach-Riegraf ◽  
Manuel Diez ◽  
Kamal Egodage ◽  
Markus Bluemel ◽  
...  
Keyword(s):  
Silicone Oil ◽  
Protein Aggregates ◽  
Imaging Analysis ◽  
Oil Droplets ◽  
Image Filter

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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