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Deep Learning models for semantic segmentation of mammography screenings
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Deep Learning-modeller för semantisk segmentering av mammografisk bildbehandling (Swedish)
Abstract [en]

This work explores the performance of state-of-the-art semantic segmentation models on mammographic imagery. It does so by comparing several reference semantic segmentation deep learning models on a newly proposed medical dataset of mammograpgy screenings. All models are re-implemented in Tensorflow and validated first on the benchmark dataset Cityscapes. The new medical image corpus was gathered and annotated at the Science for Life Laboratory in Stockholm. In addition, this master thesis shows that it is possible to boost segmentation performance by training the models in an adversarial manner after reaching convergence in the classical training framework.

Abstract [sv]

Denna uppsats undersöker hur väl moderna metoder presterar på semantisk segmentering av mammografibilder. Detta görs genom att utvärdera flera semantiska segmenteringsmetoder på ett dataset som är framtaget under detta examensarbete. Utvärderingarna genomförs genom att återimplementera flertalet semantiska segmenteringsmodeller för djupinlärning i Tensorflow och algoritmerna valideras på referensdatasetet Cityscapes. Därefter tränas modellerna också på det dataset med medicinska mammografi-bilder som är samlat och annoterat vid Science for Life Laboratory i Stockholm. Dessutom visar detta examensarbete att det är möjligt att öka segmenteringsprestandan genom att använda en adversarial träningsmetod efter att den klassiska träningsalgoritmen har konvergerat.

Place, publisher, year, edition, pages
2019. , p. 59
Series
TRITA-EECS-EX ; 2019:696
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-265652OAI: oai:DiVA.org:kth-265652DiVA, id: diva2:1380578
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Examiners
Available from: 2020-01-31 Created: 2019-12-19 Last updated: 2020-01-31Bibliographically approved

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fulltext(7576 kB)11 downloads
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