Feasibility Study for Autovalidation of Blood Cells in a Digital Morphology System
(2019) In Master's Theses in Mathematical Sciences FMAM05 20191Mathematics (Faculty of Engineering)
- Abstract
- This thesis was carried out at CellaVision, a company within digital blood analysis, with the aim of investigating the possibility of autovalidation of blood cells using neural networks. The thesis started with preparing a dataset for training and validation, including cell features and binary labels indicating if the cell is easy or difficult to classify. This dataset consisted of 122 227 cell images. The binary labels were generated through several different methods such as using neural networks, statistics from CellaVision’s system’s classification result and manual classification by a morphology expert.
Three kinds of neural networks were tested with the aim of separating easy cells from difficult ones: a binary Artificial Neural... (More) - This thesis was carried out at CellaVision, a company within digital blood analysis, with the aim of investigating the possibility of autovalidation of blood cells using neural networks. The thesis started with preparing a dataset for training and validation, including cell features and binary labels indicating if the cell is easy or difficult to classify. This dataset consisted of 122 227 cell images. The binary labels were generated through several different methods such as using neural networks, statistics from CellaVision’s system’s classification result and manual classification by a morphology expert.
Three kinds of neural networks were tested with the aim of separating easy cells from difficult ones: a binary Artificial Neural Network (ANN), an autoencoder for anomaly detection and a Self-Organizing Feature Map (SOFM) visualizing the position of difficult cells in clusters. The methods were compared and the performance were evaluated. It was found that the ANN was not useful for this task, while the autoencoder could be used for successfully autovalidating 74% of the cells. With better labeling techniques for the dataset, the performance could potentially be improved. The SOFM was not used for anomaly detection in this study, but for visual analysis of the clusters and labels. However, it may become relevant in the future to look further into this method’s possibility of anomaly detection, as patterns in
the clusters were apparent. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8978001
- author
- Branzell, Elin LU and Hellholm, Linnea
- supervisor
-
- Karl Åström LU
- organization
- alternative title
- Genomförbarhetsstudie av autovalidering av blodceller i ett digitalt morfologisystem
- course
- FMAM05 20191
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3381-2019
- ISSN
- 1404-6342
- other publication id
- 2019:E23
- language
- English
- id
- 8978001
- date added to LUP
- 2019-07-16 13:21:03
- date last changed
- 2019-07-16 13:21:03
@misc{8978001, abstract = {{This thesis was carried out at CellaVision, a company within digital blood analysis, with the aim of investigating the possibility of autovalidation of blood cells using neural networks. The thesis started with preparing a dataset for training and validation, including cell features and binary labels indicating if the cell is easy or difficult to classify. This dataset consisted of 122 227 cell images. The binary labels were generated through several different methods such as using neural networks, statistics from CellaVision’s system’s classification result and manual classification by a morphology expert. Three kinds of neural networks were tested with the aim of separating easy cells from difficult ones: a binary Artificial Neural Network (ANN), an autoencoder for anomaly detection and a Self-Organizing Feature Map (SOFM) visualizing the position of difficult cells in clusters. The methods were compared and the performance were evaluated. It was found that the ANN was not useful for this task, while the autoencoder could be used for successfully autovalidating 74% of the cells. With better labeling techniques for the dataset, the performance could potentially be improved. The SOFM was not used for anomaly detection in this study, but for visual analysis of the clusters and labels. However, it may become relevant in the future to look further into this method’s possibility of anomaly detection, as patterns in the clusters were apparent.}}, author = {{Branzell, Elin and Hellholm, Linnea}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Feasibility Study for Autovalidation of Blood Cells in a Digital Morphology System}}, year = {{2019}}, }