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Adaptive Reference Images for Blood Cells using Variational Autoencoders and Self-Organizing Maps

Odestål, Oscar LU and Palmqvist Sjövall, Anna LU (2020) In Master's Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract
CellaVision develops automated microscopy for blood analysis. Their products can pre-classify 19 different types of white blood cells and support the medical technologist performing the final classification. CellaVision provides reference cells. The reference cells are a fixed set of cells handpicked by a technologist, chosen to be typical for that specific class. The reference cells are static, i.e. the same for each cell image currently being classified and could be improved. We propose adaptive reference cells.

Using a combination of machine learning techniques, we develop a pipeline consisting of an Xception classifier, a Variational Autoencoder (VAE) and a Self-Organizing Map (SOM). This pipeline is used to produce the adaptive... (More)
CellaVision develops automated microscopy for blood analysis. Their products can pre-classify 19 different types of white blood cells and support the medical technologist performing the final classification. CellaVision provides reference cells. The reference cells are a fixed set of cells handpicked by a technologist, chosen to be typical for that specific class. The reference cells are static, i.e. the same for each cell image currently being classified and could be improved. We propose adaptive reference cells.

Using a combination of machine learning techniques, we develop a pipeline consisting of an Xception classifier, a Variational Autoencoder (VAE) and a Self-Organizing Map (SOM). This pipeline is used to produce the adaptive reference cells which are specific for each cell image. The medical technologist classifying an image is thus presented with cells that are the most visually similar to that image. The adaptive reference cells are of particular use for cells that are hard to classify.

The result consists of adaptive reference cells using both the output from the VAE and the SOM. The adaptive reference cells using the VAE are found superior. Also, cluster visualization using SOMs is presented together with proposed measurements for the robustness of the classifier and accuracy of the SOM. (Less)
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author
Odestål, Oscar LU and Palmqvist Sjövall, Anna LU
supervisor
organization
alternative title
Generering av adaptiva referensbilder till vita blodkroppar med variativ auto-kodare och självorganiserade avbildningar
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Image Analysis, Variational Autoencoder, Self-Organizing Maps, Deep Learning, Machine Learning, Microscopy, CellaVision, Microscope, Neural Networks, Blood Cells, White Blood Cells, Image Classification, Data Visualization, Clustering, Latent Space, Unsupervised Learning, Dimensionality Reduction, SOM, VAE
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3404-2020
ISSN
1404-6342
other publication id
2020:E20
language
English
id
9007379
date added to LUP
2020-04-27 14:23:48
date last changed
2020-04-27 14:23:48
@misc{9007379,
  abstract     = {{CellaVision develops automated microscopy for blood analysis. Their products can pre-classify 19 different types of white blood cells and support the medical technologist performing the final classification. CellaVision provides reference cells. The reference cells are a fixed set of cells handpicked by a technologist, chosen to be typical for that specific class. The reference cells are static, i.e. the same for each cell image currently being classified and could be improved. We propose adaptive reference cells. 

Using a combination of machine learning techniques, we develop a pipeline consisting of an Xception classifier, a Variational Autoencoder (VAE) and a Self-Organizing Map (SOM). This pipeline is used to produce the adaptive reference cells which are specific for each cell image. The medical technologist classifying an image is thus presented with cells that are the most visually similar to that image. The adaptive reference cells are of particular use for cells that are hard to classify.

The result consists of adaptive reference cells using both the output from the VAE and the SOM. The adaptive reference cells using the VAE are found superior. Also, cluster visualization using SOMs is presented together with proposed measurements for the robustness of the classifier and accuracy of the SOM.}},
  author       = {{Odestål, Oscar and Palmqvist Sjövall, Anna}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Adaptive Reference Images for Blood Cells using Variational Autoencoders and Self-Organizing Maps}},
  year         = {{2020}},
}