Faculté des Sciences appliquées
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### A workflow for large scale computer-aided cytology and its applications

##### Details
 Title : A workflow for large scale computer-aided cytology and its applications Translated title : [fr] Un workflow pour la cytologie à grande échelle assistée par ordinateur et ses applications Author : Mormont, Romain Date of defense  : 27-Jun-2016/28-Jun-2016 Advisor(s) : Geurts, Pierre Committee's member(s) : Wehenkel, Louis  Van Droogenbroeck, Marc  Marée, Raphaël Language : English Number of pages : 102 Keywords : [en] machine learning[en] cytomine[en] image processing[en] cytology[en] object detection Discipline(s) : Engineering, computing & technology > Computer science Institution(s) : Université de Liège, Liège, Belgique Degree: Master en ingénieur civil en informatique, à finalité approfondie Faculty: Master thesis of the Faculté des Sciences appliquées

##### Abstract

[en] In several fields of application, multi-gigapixel images must be analysed to gather information and take decision. This analysis is often performed manually, which is a tedious task given the volume of data to process. For instance, in cytology, branch of medical sciences which focuses on study of cells, cytopathologists analyse cell samples microscope slides in order to diagnose diseases such as cancers. Typically, malignancy is assessed by the presence or absence of cells with given characteristics. In geology, climate variations can be analysed by studying the concentration
of micro-organisms in core samples. The concentration is usually evaluated by smearing the samples onto microscope glass slides and counting those micro-organisms.

In those situations, computer sciences and, especially, machine learning and image processing provide a great alternative to a pure-human approach as they can be used to extract relevant information automatically. Especially, those kind of problems can be expressed as object detection and classification problems.

This thesis presents the elaboration and assessment of a generic framework, \textit{SLDC}, for object detection and classification in multi-gigapixel images. Especially, this framework provides implementers with a concise way of formulating problem dependent-components of their algorithm (i.e. segmentation and classification) while it takes care of problem-independent concerns such as parallelization and large image handling.

The performances of the framework are then assessed on a real-world problem, thyroid nodule malignancy. Especially, a workflow is built to detect malignant cells in thyroid cell samples whole-slides.

Results are promising: the effective processing time for an image containing 8 gigapixels is less than 10 minutes. In order, to further reduce this execution time, some improvements are proposed.

The framework implementation can be found on GitHub: https://github.com/waliens/sldc.

### Document(s)

main.pdf
Description: The framework is available at: https://github.com/waliens/sldc. Code for the application to the thyroid workflow can be found at: https://goo.gl/IqKv1u
Size: 30.37 MB
Format: Adobe PDF
Erratum_main.pdf
Description: -
Size: 30.52 MB
Format: Adobe PDF

### Author

• Mormont, Romain Université de Liège > Master ingé. civ. info., fin. appr. (ex 2e master)

### Committee's member(s)

• Wehenkel, Louis Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
View his publications on ORBi
• Van Droogenbroeck, Marc Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
View his publications on ORBi
• Marée, Raphaël Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
View his publications on ORBi
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• Total number of downloads 331

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