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Organ Segmentation Using Deep Multi-task Learning with Anatomical Landmarks
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Segmentering av organ med multi-task learning och anatomiska landmärken (Swedish)
Abstract [en]

This master thesis is the study of multi-task learning to train a neural network to segment medical images and predict anatomical landmarks. The paper shows the results from experiments using medical landmarks in order to attempt to help the network learn the important organ structures quicker. The results found in this study are inconclusive and rather than showing the efficiency of the multi-task framework for learning, they tell a story of the importance of choosing the tasks and dataset wisely. The study also reflects and depicts the general difficulties and pitfalls of performing a project of this type.

Place, publisher, year, edition, pages
2018. , p. 39
Series
TRITA-CBH-GRU ; 2019:005
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-241640OAI: oai:DiVA.org:kth-241640DiVA, id: diva2:1282190
External cooperation
NOVAMIA
Subject / course
Medical Engineering
Educational program
Master of Science in Engineering - Medical Engineering
Supervisors
Examiners
Available from: 2019-02-01 Created: 2019-01-24 Last updated: 2019-02-01Bibliographically approved

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School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH)
Medical Image Processing

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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