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Providing Mass Context to a Pretrained Deep Convolutional Neural Network for Breast Mass Classification
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Att tillhandahålla masskontext till ett förtränat djupt konvolutionellt neuralt nätverk för klassificering av bröstmassa (Swedish)
Abstract [en]

Breast cancer is one of the most common cancers among women in the world, and the average error rate among radiologists during diagnosis is 30%. Computer-aided medical diagnosis aims to assist doctors by giving them a second opinion, thus decreasing the error rate. Convolutional neural networks (CNNs) have shown to be good for visual detection and recognition tasks, and have been explored in combination with transfer learning. However, the performance of a deep learning model does not only rely on the model itself, but on the nature of the dataset as well

In breast cancer diagnosis, the area surrounding a mass provides useful context for diagnosis. In this study, we explore providing different amounts of context to the CNN model ResNet50, to see how it affects the model’s performance. We test masses with no additional context, twice the amount of original context and four times the amount of original context, using 10-fold cross-validation with ROC AUC and average precision (AP ) as our metrics. The results suggest that providing additional context does improve the model’s performance. However, giving two and four times the amount of context seems to give similar performance.

Abstract [sv]

Bröstcancer är en av de vanligaste cancersjukdomar bland kvinnor i världen, och den genomsnittliga felfrekvensen under diagnoser är 30%. Datorstödd medicinsk diagnos syftar till att hjälpa läkare genom att ge dem en andra åsikt, vilket minskar felfrekvensen. Konvolutionella neurala nätverk (CNNs) har visat sig vara bra för visuell detektering och igenkännande, och har utforskats i samband med det s.k. “transfer learning”. Prestationen av en djup inlärningsmodell är däremot inte enbart beroende på modellen utan också på datasetets natur. I bröstcancerdiagnos ger området runt en bröstmassa användbar kontext för diagnos. I den här studien testar vi att ge olika mängder kontext till CNNmodellen ResNet50, för att se hur det påverkar modellens prestanda. Vi testar bröstmassor utan ytterligare kontext, dubbelt så mycket som den originala mängden kontext och fyra gånger så mycket som den orginala mängden kontext, med hjälp av “10-fold cross-validation” med ROC AUC och “average precision” (AP ) som våra mätvärden. Resultaten visar att mer kontext förbättrar modellens prestanda. Däremot verkar att ge två och fyra gånger så mycket kontext resultera i liknande prestanda.

Place, publisher, year, edition, pages
2019. , p. 38
Series
TRITA-EECS-EX ; 2019:364
Keywords [en]
Deep learning; Convolutional neural network; Transfer learning; Computer-aided diagnosis; Breast cancer; Breast mass classification; Breast mass context;
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259993OAI: oai:DiVA.org:kth-259993DiVA, id: diva2:1354169
Supervisors
Examiners
Available from: 2019-10-02 Created: 2019-09-24 Last updated: 2019-10-02Bibliographically approved

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