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Nuclear Segmentation Using Convolutional Neural Networks with Limited Training Data

Title
Nuclear Segmentation Using Convolutional Neural Networks with Limited Training Data
Authors
Mungi Kwon
DGIST Authors
Kwon, Mungi; Hwang, JaeyounPark, Sanghyun
Advisor(s)
박상현
Co-Advisor(s)
Jaeyoun Hwang
Issue Date
2020
Available Date
2020-06-23
Degree Date
2020-02
Type
Thesis
Description
Nuclear, Instance segmentation, Deep convolutional neural network, Watershed algorithm, Siamese network, Using unlabeled data, Label smoothing
Table Of Contents
Ⅰ. INTRODUCTION 1 1.1 Introduction 1 1.2 Related works 2 ⅠⅠ. Method 4 2.1 Nuclei segmentation using deep learning based watershed algorithm 4 A. Deep learning based watershed algorithm 4 B. Implementation details 5 2.2 Separation of adjacent nuclei using a Siamese neural network 5 A. Overall procedure 6 B. Proposed network 7 C. Implementation details 8 2.3 Segmentation using unlabeled data 9 A. Label smoothing 9 B. Implementation details 10 ⅠⅠⅠ. Experimental results 10 3.1 Data set 10 3.2 Evaluation metrics 11 3.3 Comparison methods 11 3.4 Segmentation accuracy 13 3.5 Classification accuracy 13 3.6 Accuracy with labeled and unlabeled data 15 VI. Conclusion 17 References 18
URI
http://dgist.dcollection.net/common/orgView/200000285883
http://hdl.handle.net/20.500.11750/11958
DOI
10.22677/Theses.200000285883
Degree
Master
Department
Robotics Engineering
University
DGIST
Related Researcher
  • Author Hwang, Jae Youn MBIS(Multimodal Biomedical Imaging and System) Laboratory
  • Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
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Collection:
Department of Robotics EngineeringThesesMaster


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