Nuclear Segmentation Using Convolutional Neural Networks with Limited Training Data
- Title
- Nuclear Segmentation Using Convolutional Neural Networks with Limited Training Data
- Author(s)
- Mungi Kwon
- DGIST Authors
- Hwang, Jaeyoun ; Kwon, Mungi ; Park, Sanghyun
- Advisor
- 박상현
- Co-Advisor(s)
- Jaeyoun Hwang
- Issued Date
- 2020
- Awarded 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
- Degree
- Master
- Department
- Robotics Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Hwang, Jae Youn
- Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
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- Files in This Item:
-
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- Appears in Collections:
- Department of Robotics and Mechatronics Engineering Theses Master
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