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Employing Attention-Based Learning For Medical Image Segmentation
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
Användande av uppmärksamhetsbegrepp för medicinsk bildsegmentering (Swedish)
Abstract [en]

Automated medical image analysis is a non-trivial task due to the complexity of medical data. With the advancements made on Computer Vision through the golden era of Deep Learning, many models which rely on Deep Convolutional Networks have emerged in the Medical Imaging domain and offer important contributions in automating the analysis of medical images. Based on recent literature, this work proposes the adaptation of visual attention gates in Fully Convolutional Encoder-Decoder networks in the Medical Image Segmentation task. Appropriate data pre-processing is performed in the cases of 2-dimensional and 3-dimensional data in order to serve them as proper inputs in conventional and attention-gated Deep Convolutional Networks that try to identify classes in pixel and voxel level respectively. Attention gates can be easily integrated in the conventional networks, that would improve their performance. We present the specific mechanics of attention gates, conduct experiments and analyse our derived results. Finally, based on the latter, we provide our opinion and intuition on how this work can be further expanded towards new research directions.

Abstract [sv]

Automatiserad analys av medicinska bilder är en icke-trivial uppgift på grund av komplexiteten i medicinsk data. Med framstegen som gjorts inom datorseende i samband med den gyllene eran av djupinlärning har många modeller som använder sig av djupa faltningsnätverk kommit fram inom domänen av medicinska bilder, och erbjuder viktiga bidrag till automatiseringen av medicinsk bildanalys. Baserat på senare litteratur, föreslår detta examensarbete anpassning av visual attention gates för fully convolutional encoder-decoder networks för segmentering av medicinska bilder. Lämplig förbehandling av data har utförts för fallen 2-dimensionell och 3-dimensionell data för att passa som inmatningsvärden till konventionella och attention-gated, djupa faltningsnätverk som försöker identifiera klasser i respektive pixeloch voxel nivå. Attention gates kan lätt integreras med konventionella nätverk så att dess prestanda förbättras. Vi presenterar de specifika mekanismerna hos attention gates, utför experiment och analyserar våra framtagna resultat. Slutligen, baserat på resultaten, ger vi våra åsikter och intuitioner om hur detta arbete kan byggas ut ytterligare för forskning i nya riktningar.

Place, publisher, year, edition, pages
2019. , p. 53
Series
TRITA-EECS-EX
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-271185OAI: oai:DiVA.org:kth-271185DiVA, id: diva2:1415908
Subject / course
Computer Science
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-03-20Bibliographically approved

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