{"id":84933,"date":"2019-04-23T14:25:25","date_gmt":"2019-04-23T12:25:25","guid":{"rendered":"https:\/\/uniavisen.dk\/event\/ph-d-forsvar-paa-datalogisk-institut-af-silas-nyboe-oerting\/"},"modified":"2019-04-23T14:25:25","modified_gmt":"2019-04-23T12:25:25","slug":"ph-d-forsvar-paa-datalogisk-institut-af-silas-nyboe-oerting","status":"publish","type":"event","link":"https:\/\/uniavisen.dk\/en\/event\/ph-d-forsvar-paa-datalogisk-institut-af-silas-nyboe-oerting\/","title":{"rendered":"Ph.d.-forsvar p\u00e5 Datalogisk Institut af Silas Nyboe \u00d8rting"},"content":{"rendered":"

PhD defence Silas Nyboe \u00d8rting<\/p>\n

Assessing emphysema in CT scans of the lungs using machine learning, crowdsourcing and visual similarity<\/h3>\n

Date:<\/strong>\u00a026 April 2019, at 13.15<\/p>\n

Place:<\/strong>\u00a0Auditorium 04 \u2013 HC\u00d8, Universitetsparken 5, 2100 Kbh. \u00d8<\/p>\n

Abstract<\/strong>
\nEmphysema is a pathology in chronic obstructive pulmonary disease (COPD) characterized by destruction of lung tissue. The extent and appearance of emphysema can be assessed in CT scans of the lungs. Current recommendations for assessing emphysema in CT scans is to use a combination of densitometry and visual assessment.<\/p>\n

Current densitometry methods are vulnerable to variation in scanners, scan protocols and software implementations, and cannot characterize emphysema patterns. Visual assessment requires expert knowledge, is time-consuming and commonly only provides semi-quantitative estimates of emphysema extent.<\/p>\n

Machine learning methods that learn from visual assessment could combine the benefits of densitometry and visual assessment, to provide fully automated assessment of emphysema extent and patterns. One of the main issues when applying supervised machine learning in medical image analysis is obtaining labels.<\/p>\n

This thesis investigates three approaches to reducing the need for labels when training machine learning methods to assess emphysema: weakly supervised learning, crowdsourcing and learning from visual similarity. Weakly supervised machine learning aims at learning from global labels instead of local labels, for example learning from image labels instead of pixel labels. The thesis investigates emphysema detection and quantification within two weakly supervised learning settings, multiple instance learning, where labels are binary, and learning with label proportions, where labels are proportions. Crowdsourcing aims at reducing labeling costs by replacing expert annotators with non-experts.<\/p>\n

The thesis provides a survey of how crowdsourcing is used in medical imaging, as well as an investigation into how emphysema assessment can be framed as a task that can be solved by non-expert crowd workers. Learning from visual similarity aims at learning representations from relative comparisons of images. The thesis investigates how visual similarity assessments can be obtained and used for training convolutional neural networks to learn representations of chest CT scans.<\/p>\n

Assessment Committee:<\/strong>
\nAssociate Professor, Erik Dam,Department of Computer Science, University of Copenhagen\u00a0(Head of committee)
\nAssociate Professor, Elsa Angelini, Imperial College London
\nAssociate Professor, Georg Langs,\u00a0Medical University of Vienna<\/p>\n

Academic supervisor:<\/strong>
\nProfessor Marleen de Bruijne,\u00a0Department of Computer Science, University of Copenhagen
\nAssistant Professor, Jens Petersen,\u00a0Department of Computer Science, University of Copenhagen<\/p>\n

For an electronic copy of the thesis, please contact\u00a0phdadmin@di.ku.dk<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

PhD defence Silas Nyboe \u00d8rting Assessing emphysema in CT scans of the lungs using machine learning, crowdsourcing and visual similarity Date:\u00a026 April 2019, at 13.15 Place:\u00a0Auditorium 04 \u2013 HC\u00d8, Universitetsparken 5, 2100 Kbh. \u00d8 Abstract Emphysema is a pathology in chronic obstructive pulmonary disease (COPD) characterized by destruction of lung tissue. The extent and appearance […]<\/p>\n","protected":false},"author":0,"featured_media":84934,"template":"","class_list":["post-84933","event","type-event","status-publish","has-post-thumbnail","hentry","event_category-phd"],"acf":[],"aioseo_notices":[],"yoast_head":"\nPh.d.-forsvar p\u00e5 Datalogisk Institut af Silas Nyboe \u00d8rting<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/en\/event\/ph-d-forsvar-paa-datalogisk-institut-af-silas-nyboe-oerting\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ph.d.-forsvar p\u00e5 Datalogisk Institut af Silas Nyboe \u00d8rting\" \/>\n<meta property=\"og:description\" content=\"PhD defence Silas Nyboe \u00d8rting Assessing emphysema in CT scans of the lungs using machine learning, crowdsourcing and visual similarity Date:\u00a026 April 2019, at 13.15 Place:\u00a0Auditorium 04 \u2013 HC\u00d8, Universitetsparken 5, 2100 Kbh. \u00d8 Abstract Emphysema is a pathology in chronic obstructive pulmonary disease (COPD) characterized by destruction of lung tissue. 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