Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNguyen, Xuan Phi
dc.description.abstractMedical images, such as X-Ray, Computed Topographic (CT) or Magnetic Resonance Imaging (MRI), requires expertise and patience to interpret. In clinical practice, only extensively trained specialists, also called radiologists, are qualified to read and provide observation report to physicians to assist their diagnosis. Due to the rapidly increasing demand of medical imaging, the radiologists are to handle a significant number of images every day. Thus, this is a huge pressure for them and life-threatening errors are likely to be made due to observers’ fatigue and rushing actions. Automatic medical image segmentation has been drawing considerable attention recently. This is because it has the potential to drastically reduce the workload of radiologists, automate and accelerate the process as well as improve diagnosis accuracy. Recent advances in deep learning techniques may make this technology possible to satisfy the strict requirements of clinical practice. Nonetheless, current state-of-the-art neural network methodologies still face a number of challenges. In particular, they lacks a sufficient amount of annotated medical data as qualified radiologists are often occupied. Errors and missing segments in the annotated data are also common. Medical images also tend to be too ambiguous for computer and data science professionals to understand without certain medical background. Finally, existing techniques to medical image segmentation also have inherent limitations.The objective of this project is to examine various deep learning approaches to medical image segmentation, investigate limitations in this line of research as well as propose novel methods to overcome current disadvantages and improve performances both experimentally and clinically. In the scope of the project, a variety of novel methods have been proposed. They were tested on three different segmentation tasks for medical anomalies and anatomical organs. Among them, four solutions have been experimentally shown to be effective and significantly con- tribute performance gain in segmentation. Two of them have been included into a conference paper entitled ”Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net”, which has been accepted for presentation at the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (IEEE BHI 2019), Chicago, USA, May 2019.en_US
dc.format.extent59 p.en_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleCapsnet and ensemble of deep learning for medical image segmentationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHuang Weiminen_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.supervisorLu Zhongkang
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
  Restricted Access
3.05 MBAdobe PDFView/Open

Page view(s)

Updated on Nov 25, 2023


Updated on Nov 25, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.