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Automated Lung Tumor Diagnosis in Medical Image Data - Methods, Challenges and Perspectives

  • Cancer is one of the leading causes of death worldwide [183], with lung tumors being the most frequent cause of cancer deaths in men as well as one of the most common cancers diagnosed in woman [40]. As symptoms often arise in advanced stages, an early diagnosis is especially important to ensure the best and earliest possible treatment. In order to achieve this, Computed Tomography (CT) scans are frequently used for tumor detection and diagnosis. We will present examples of publicly available CT image data of lung cancer patients and discuss possible methods to realize an automatic system for automated cancer diagnosis. We will also look at the recent SPIE-AAPM Lung CT Challenge [10] data set in detail and describe possible methods and challenges for image segmentation and classification based on this data set.

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Document Type:Conference Object
Author:Tim Adams
Parent Title (English):Klewitz-Hommelsen, Lang, Schönbach (Eds.): Science Track FrOSCon 2018
Number of pages:6
First Page:13
Last Page:18
Publisher:Hochschule Bonn-Rhein-Sieg
Place of publication:Sankt Augustin
Date of first publication:2021/06/24
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten
Conference volumes:FrOSCon - Free and Open Source Software Conference / 13. Free and Open Source Conference (FrOSCon), Sankt Augustin, 25.-26.08.2018
Entry in this database:2021/06/24