請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90984完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃升龍 | zh_TW |
| dc.contributor.advisor | Sheng-Lung Huang | en |
| dc.contributor.author | 劉智皓 | zh_TW |
| dc.contributor.author | Chih-Hao Liu | en |
| dc.date.accessioned | 2023-10-24T16:36:39Z | - |
| dc.date.available | 2025-08-01 | - |
| dc.date.copyright | 2023-10-24 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-26 | - |
| dc.identifier.citation | Bancroft, J. D., & Gamble, M. (2008). Theory and practice of histological techniques. Elsevier health sciences.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90984 | - |
| dc.description.abstract | 醫學影像分析不論是在醫學、工業還是學術界一向為一個錯綜複雜的問題。以定量和定性地解釋醫學影像需要長期的經驗和專業知識的累積。以光學同調斷層掃描（Optical coherence tomography, OCT）影像而言，其具備非侵入性和高速診斷的優勢，甚至近幾年已達到細胞級解析度的水準。然而在OCT影像中，對人體皮膚結構和病變的研究仍然有限，所以本論文的目標是通過開發一系列深度學習演算法和模型架構，來探索微米解析度等級的人體皮膚OCT影像。
為了能夠更系統性的分析和理解，本論文總共劃分成四個大項。第一大項為「定量理解」。一開始將開發二維影像分割模型，分割人類皮膚層和細胞核，並探討二維模型的侷限性，之後則進一步開發非監督式影像去雜訊模型先改善三維影像的品質，再發展半監督式三維影像分割模型解決二維模型的限制，並分析人類角質細胞核的大小。第二大項為「定性理解」。此項目中將透過開發生成式模型轉換OCT和醫學中常見的蘇木精與伊紅(Hematoxylin and eosin, H&E)染色切片影像，來理解人類皮膚組織中兩種影像的對應性，而這個部分會先以非監督式學習的方法來初步了解轉換上的效果和限制，之後將進一步利用影像標註來增加轉換的準確性。 基於前兩大項中的知識背景，接下來將探討具有疾病的人類皮膚OCT影像。而第三大項為「定性解釋」。此項目將開發一系列影像辨識模型來區別不同的疾病，並透過可解釋性人工智慧框架Grad-CAM用以辨別各個疾病的病兆，其中將先以自監督式學習開發常見皮膚疾病的辨識模型，接著以元學習開發數據量非常少的罕見疾病辨識模型，最後將針對長尾資料分布情境，開發一套可辨識健康狀態和13種皮膚疾病的模型。第四大項為「定量解釋」。在第三大項中，由於神經網絡可解釋性上的限制，無法進一步解釋疾病的嚴重程度和不同情境定量上的判斷依據，所以此項目將先以多任務學習強化二維影像分割模型在疾病影像的適應性，以及賦予模型自動辨識出黑色素分布的能力，並以此分割結果製作一系列的指標和特徵來開發可定量解釋的機器學習模型。 對於本論文的主要貢獻不僅在OCT影像上分析人體皮膚提供定量和定性的視角，並更進一步奠定了可信賴人工智慧和精準醫療診斷的基礎。 | zh_TW |
| dc.description.abstract | Medical image analysis is a convoluted and involuted problem whether in the field of medicine, industry, or academia. Quantitatively and qualitatively interpreting the medical data invariably and inevitably necessitate the long-term experience and long-standing expertise. While the progress of the cellular-resolution optical coherence tomography (OCT) present the opportunity for non-invasive and high-speed diagnosis and providing histopathological-level information, limited amounts of research have explored the human skin structures and lesions in such images. The advancement of the deep learning paves the way for systematically and automatically analyzing the medical images. Therefore, the objective of this thesis is exploring the human skin cellular-resolution OCT imaging through developing a series of deep learning approaches.
In order to facilitate a more holistic analysis, this thesis is divided into four major components. The first component is "Quantitative Comprehension." Initially, a 2D image segmentation model will be developed to segment healthy human skin layers and cell nuclei, investigating the limitations of the two-dimensional model. Subsequently, an unsupervised image denoising model will be developed to improve the quality of 3D volume, followed by the development of a semi-supervised 3D image segmentation model to address the limitations of the two-dimensional model, analyzing the size of human keratinocytes cell nuclei. The second component is "Qualitative Comprehension." In this aspect, a generative model will be developed to transform OCT images into commonly used hematoxylin and eosin (H&E) stained slides, in order to understand the correspondence between the two types of images in human skin tissue. This part will initially employ unsupervised learning methods to gain preliminary insights into the effectiveness and limitations of the transformation. Subsequently, image annotation will be utilized to enhance the accuracy of the transformation. Based on the knowledge background from the previous two components, the subsequent exploration will focus on unhealthy human skin. The third component is "Qualitative Interpretation." This involves developing a series of image recognition models to differentiate various diseases and employing the explainable artificial intelligence framework, Grad-CAM, to identify disease-specific signs. Initially, a self-supervised learning approach will be used to develop an image recognition model for common skin diseases. Subsequently, meta-learning techniques will be utilized to develop recognition models for scarce diseases with limited data. Ultimately, the model for the long-tail data distribution is developed to form a comprehensive system capable of recognizing both healthy conditions and 13 skin diseases. The fourth component is "Quantitative Interpretation." Due to the limitations in the interpretability of neural networks, further quantitative assessments of disease severity and context-dependent judgments cannot be made. Consequently, this component will begin with multi-task learning to enhance the adaptability of the segmentation model in disease images and identify melanin distribution. Based on the segmentation results, a set of indicators and features will be generated to develop an interpretable machine learning model. The main contribution of this thesis not only provides the quantitative and qualitative perspective for analyzing human skin but also lays the first stone toward trustworthy artificial intelligence and accurate medical diagnosis. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-24T16:36:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-24T16:36:39Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgement i
中文摘要 ii Abstract iii Content iv List of Figures xv List of Tables xxxiv Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 1 Chapter 2 Optical Coherence Tomography 4 2.1 Michelson interferometer 4 2.2 Time-domain OCT system 5 2.3 Full-field OCT system 8 2.4 Mirau-based FF-OCT system 9 2.4.1 Ce3+:YAG light source 11 2.4.2 Ti:sapphire light source 12 Chapter 3 Human Skin Tissue and Lesions 14 3.1 Human skin tissue structure 14 3.1.1 Epidermis 15 3.1.1.1 Stratum corneum 15 3.1.1.2 Stratum lucidum 16 3.1.1.3 Stratum granulosum 16 3.1.1.4 Stratum spinosum 16 3.1.1.5 Stratum basale 17 3.1.2 Dermis 17 3.1.2.1 Papillary layer 17 3.1.2.2 Reticular layer 18 3.1.3 Hypodermis 18 3.2 Hematoxylin and eosin stains 18 3.2.1 H&E stains for human skin 18 3.2.2 Preparation procedure 19 3.3 Human skin lesions 22 3.3.1 Inflammatory lesions 22 3.3.1.1 Eczema 22 3.3.1.2 Psoriasis 23 3.3.2 Pigmented lesions 24 3.3.2.1 Solar lentigo 24 3.3.2.2 Melasma 25 3.3.2.3 Vitiligo 26 3.3.3 Benign tumor 26 3.3.3.1 Nevus 27 3.3.3.2 Seborrhoeic keratosis 28 3.3.3.3 Hemangioma 28 3.3.4 Malignant tumor 29 3.3.4.1 Actinic keratosis 29 3.3.4.2 Bowen’s disease 30 3.3.4.3 Melanoma 31 3.3.4.4 Basal cell carcinoma 32 3.3.4.5 Squamous cell carcinoma 33 Chapter 4 Machine Learning 34 4.1 Supervised learning 34 4.2 Unsupervised learning 35 4.3 Deep learning 36 4.3.1 Deep neural network 37 4.3.2 Backpropagation 38 4.4 Transfer learning 39 4.5 Curriculum learning 40 4.6 Multi-task learning 41 4.7 Semi-supervised learning 41 4.7.1 Co-training 42 4.7.2 Self-training 42 4.8 Self-supervised learning 44 4.8.1 Generative method 44 4.8.2 Contrastive method 44 4.9 Meta-learning 46 4.10 Long-tail learning 48 4.10.1 Cost-sensitive learning 50 4.10.2 Post-hoc logit adjustment 50 4.11 Explainable machine learning 51 4.11.1 Local interpretable model-agnostic explanations 52 4.11.2 Shapley additive explanations 53 Chapter 5 Computer Vision using Deep Learning 55 5.1 Convolutional neural networks 55 5.2 Image classification 56 5.2.1 ResNet 56 5.2.2 Inception V3 57 5.3 Image segmentation 58 5.3.1 U-Net 58 5.3.2 3D U-Net 59 5.3.3 V-Net 60 5.3.4 StarDist 61 5.4 Image denoising 62 5.4.1 Denoising autoencoder 62 5.5 Image generation 63 5.5.1 Variational autoencoder 64 5.5.2 Generative adversarial network 66 5.6 Image translation 69 5.6.1 Supervised image-to-image translation 69 5.6.2 Unsupervised image-to-image translation 71 5.7 Visual explanations 79 5.7.1 Class activation map 79 5.7.2 Grad-CAM 80 5.7.3 Grad-CAM++ 81 Chapter 6 Skin Image Dataset 83 6.1 Laboratory in-vivo FF-OCT dataset 83 6.1.1 Data collection 83 6.1.2 Data pre-processing 84 6.1.3 Data annotation 85 6.1.4 Exploratory data analysis 85 6.2 Clinical H&E dataset 87 6.2.1 Data collection 87 6.2.2 Data pre-processing 87 6.2.3 Data annotation 88 6.2.4 Exploratory data analysis 88 6.3 Clinical in-vivo FF-OCT dataset 90 6.3.1 Data collection 90 6.3.2 Data pre-processing 91 6.3.3 Data annotation 92 6.3.4 Exploratory data analysis 96 Chapter 7 Healthy Human Skin: 2D and 3D Segmentation 102 7.1 2D skin layers and keratinocytes segmentation 102 7.1.1 Dataset 102 7.1.2 Image pre-processing 103 7.1.3 Model architecture 104 7.1.4 Metrics and loss function 106 7.1.5 Post-processing 107 7.1.6 Experiments 108 7.1.7 Results 109 7.1.7.1 Quantitative segmentation results 109 7.1.7.2 Qualitative segmentation results 110 7.1.7.3 Post-processing results 111 7.1.7.4 Skin layer thickness analysis 112 7.1.7.5 Cell nuclei area analysis 113 7.1.8 Labeling criteria and inconsistency 114 7.1.8.1 Metrics sensitivity 115 7.1.8.2 Dataset diversity 117 7.1.8.3 Labeling inconsistency 118 7.1.9 Summary 119 7.2 3D cross-sectional image denoising 120 7.2.1 Dataset 120 7.2.2 Image pre-processing 120 7.2.3 Method 122 7.2.4 Experiment 123 7.2.4.1 Data pipeline 123 7.2.4.2 Training details 124 7.2.5 Results 125 7.2.5.1 Qualitative results 125 7.2.5.1 Quantitative results 126 7.2.6 Summary 127 7.3 3D cell nuclei segmentation 128 7.3.1 Dataset 128 7.3.2 Image pre-processing 128 7.3.3 Method 129 7.3.3.1 Cross-sectional profile denoising 130 7.3.3.2 3D cell nuclei pseudo label generation 130 7.3.3.3 3D segmentation model 132 7.3.4 Experiment 133 7.3.5 Results 134 7.3.5.1 2D self-training segmentation results 134 7.3.5.2 3D U-Net segmentation results 135 7.3.5.3 Comparison with annotation 138 7.3.5.4 3D volume statistical analysis 139 7.3.6 Limitation 140 7.3.7 Summary 141 7.4 Discussion for quantitative comprehension 142 Chapter 8 Healthy Human Skin: H&E Translation 143 8.1 Unsupervised OCT and H&E image translation 143 8.1.1 Dataset 144 8.1.2 Method 145 8.1.2.1 Blur the excessive features 146 8.1.2.2 Defend the adversarial attack 146 8.1.2.3 Stabilize the inverse translation 147 8.1.2.4 Mitigate the speckle noise 148 8.1.2.5 Full objective function 148 8.1.3 Experiment 148 8.1.3.1 Baselines 148 8.1.3.2 Metrics 149 8.1.4 Results 149 8.1.4.1 Qualitative results 149 8.1.4.2 Quantitative results 151 8.1.4.3 Transfer learning results 151 8.1.5 Analysis 153 8.1.5.1 Effect of the noise 153 8.1.5.2 Weight of the noise 155 8.1.6 Limitation 155 8.1.7 Summary 157 8.2 Label-assisted OCT and H&E image translation 158 8.2.1 Dataset 158 8.2.2 Method 158 8.2.2.1 OCT-to-H&E direction 159 8.2.2.2 H&E-to-OCT direction 160 8.2.2.3 Adversarial function 161 8.2.2.4 Integrated with SNRGAN 162 8.2.3 Experiment 163 8.2.3.1 Baselines 163 8.2.3.2 Metrics 164 8.2.4 Results 164 8.2.4.1 Qualitative results 164 8.2.4.2 Quantitative results 166 8.2.5 Ablation studies 167 8.2.5.1 Assisted networks 167 8.2.5.2 Model architectures 171 8.2.5.3 Noise effect 175 8.2.6 Limitations 176 8.2.7 Summary 176 8.3 Discussion for qualitative comprehension 178 Chapter 9 Unhealthy Human Skin: Lesions Classification 179 9.1 General skin lesions classification 179 9.1.1 Dataset 180 9.1.2 Method 180 9.1.2.1 Self-supervised learning 180 9.1.2.2 Curriculum learning 181 9.1.2.3 Overall model learning scheme 182 9.1.3 Experiment 182 9.1.3.1 Model training 182 9.1.3.2 Model evaluation 183 9.1.4 Results 183 9.1.4.1 Self-supervised fine-tuning results 183 9.1.4.2 Easy task fine-tuning results 188 9.1.4.3 Hard task fine-tuning results 188 9.1.5 Analysis 190 9.1.6 Summary 192 9.2 Scarce Skin lesions classification 193 9.2.1 Dataset 193 9.2.2 Method 194 9.2.2.1 Self-supervised pre-training 194 9.2.2.2 Meta learning 194 9.2.3 Experiments 195 9.2.3.1 Meta training 195 9.2.3.2 Meta testing 196 9.2.4 Results 196 9.2.5 Analysis 199 9.2.6 Summary 201 9.3 Long-tail distribution classification 202 9.3.1 Dataset 203 9.3.2 Method 203 9.3.2.1 Cost-sensitive learning 203 9.3.2.2 Post-hoc logit adjustment 204 9.3.3 Experiment 204 9.3.4 Results 204 9.3.4.1 Compare with general skin lesion model 205 9.3.4.2 Compare with scarce skin lesion model 209 9.3.5 Analysis 213 9.3.6 Limitations 215 9.3.6.1 Cross-sectional view 215 9.3.6.2 Unexplainable inference 216 9.3.7 Summary 217 9.4 Discussion for qualitative interpretation 218 Chapter 10 Unhealthy Human Skin: Explainable AI 219 10.1 Melanin localization 219 10.1.1 Dataset 219 10.1.2 Method 221 10.1.3 Experiment 221 10.1.4 Results 222 10.1.4.1 Qualitative results 222 10.1.4.2 Quantitative results 226 10.1.5 Analysis 226 10.1.5.1 Normal skin evaluation 226 10.1.5.2 Inflammatory lesions evaluation 227 10.1.5.3 Pigmented lesions evaluation 229 10.1.5.4 Benign tumor evaluation 231 10.1.5.5 Malignant tumor evaluation 233 10.1.6 Statistical analysis for general skin lesions 236 10.1.6.1 Eczema statistical analysis 236 10.1.6.2 Psoriasis statistical analysis 238 10.1.6.3 Solar lentigo statistical analysis 240 10.1.6.4 Vitiligo statistical analysis 242 10.1.6.5 Nevus statistical analysis 244 10.1.6.6 Seborrhoeic keratosis statistical analysis 246 10.1.6.7 Quantitative comparison 248 10.1.7 Limitations 249 10.1.8 Summary 250 10.2 Explainable skin lesions classification 251 10.2.1 Dataset 251 10.2.2 Method 252 10.2.2.1 Feature engineering 252 10.2.2.2 Interpretable machine learning model 254 10.2.3 Experiment 255 10.2.3.1 Model training 255 10.2.3.2 Shapley additive explanations 255 10.2.4 Results 256 10.2.4.1 Eczema case 256 10.2.4.2 Psoriasis case 258 10.2.4.3 Solar lentigo case 260 10.2.4.4 Vitiligo case 263 10.2.4.5 Nevus case 265 10.2.4.6 Seborrhoeic keratosis case 267 10.2.5 Analysis 269 10.2.6 Limitations 271 10.2.6.1 Feature selection 271 10.2.6.2 Feature representative 272 10.2.7 Summary 272 10.3 Discussion for quantitative interpretation 273 Chapter 11 Overall Discussion 274 11.1 Optical system perspective 274 11.2 Clinical diagnosis perspective 275 11.3 Computer vision perspective 276 11.4 Social ethics perspectives 277 11.5 Big data and good data 279 Chapter 12 Conclusion 280 12.1 Contributions 280 12.2 Future works 283 12.2.1 Short-term objectives 283 12.2.2 Long-term objectives 285 12.2.3 Summary 286 Reference 288 Appendix 301 A.1 List of figures in appendix 301 A.2 List of tables in appendix 305 A.3 Monte-Carlo simulation pre-trained model 306 A.3.1 Dataset 306 A.3.2 Method 306 A.3.2.1 Tissue model building 307 A.3.2.2 Multi-layers Monte-Carlo simulation 308 A.3.2.3 Optical coherence tomography simulation 311 A.3.2.4 Bias Henyey-Greenstein function 311 A.3.2.5 Model pre-training and fine-tuning 313 A.3.3 Experiments 313 A.3.3.1 Monte-Carlo OCT simulation 313 A.3.3.2 Model pre-training 314 A.3.3.3 Model fine-tuning 314 A.3.3.4 Model comparison 314 A.3.4 Results 314 A.3.5 Summary 315 A.4 Cell segmentation with morphological filtering 316 A.4.1 Dataset 316 A.4.2 Method 316 A.4.2.1 Image pre-processing 316 A.4.2.2 Intensity thresholding 317 A.4.2.3 Morphological filtering 318 A.4.2.4 Morphology refining with deep learning 319 A.4.3 Experiments 319 A.4.4 Results 319 A.4.5 Summary 320 A.5 Dynamics FF-OCT image denoising 321 A.5.1 Dataset 321 A.5.2 Method 322 A.5.3 Experiments 323 A.5.4 Results 324 A.5.4.1 Cropped image denoising results 324 A.5.4.2 Whole image denoising results 324 A.5.5 Limitations 325 A.5.6 Summary 326 A.6 3D H&E volume translation 327 A.6.1 Method 327 A.6.2 Results 327 A.6.2.1 Cross-sectional slices translation results 327 A.6.2.1 En-face translation results 328 A.6.3 Limitations 329 A.6.4 Summary 330 A.7 Melanin translation with domain adaptation 331 A.7.1 Dataset 332 A.7.2 Method 332 A.7.3 Experiment 333 A.7.4 Results 333 A.7.4.1 Ce3+:YAG to Ti:sapphire image translation 333 A.7.4.2 Ti:sapphire to Ce3+:YAG image translation 334 A.7.5 Melanin translation 335 A.7.6 Limitations 335 A.7.7 Summary 336 A.8 3D segmentation model for skin lesions: concept 337 A.8.1 Method 337 A.8.1.1 2D semi-supervised learning scheme 337 A.8.1.2 3D semi-supervised learning scheme 337 A.8.2 Anticipated challenges 339 A.9 3D C-scan and 2D B-scan registration: concept 340 A.9.1 Method 340 A.9.1.1 VoxelMorph 340 A.9.1.2 OCT image registration with VoxelMorph 342 A.9.2 Anticipated challenges 343 A.10 Weakly supervised blood vessel extraction: concept 344 A.10.1 Method 344 A.10.2 Anticipated challenges 344 A.11 Multi-domains skin images translation: concept 345 A.11.1 Method 345 A.11.1.1 StarGAN 345 A.11.1.2 OCT, H&E, T-Blue, and IHC translation 347 A.11.2 Anticipated challenges 348 A.12 Multi-input lesions classification: concept 350 A.12.1 Method 350 A.12.1.1 DiRA 351 A.12.1.2 Multi-inputs with OCT and dermoscopy data 352 A.12.2 Anticipated challenges 353 A.13 Multiple-instance lesions classification: concept 354 A.13.1 Method 354 A.13.1.1 Dual-stream multiple-instance learning 354 A.13.1.2 Lesions identification with multiple-instance 356 A.13.2 Anticipated challenges 356 A.14 Multi-label lesions classification: concept 357 A.14.1 Method 357 A.14.2 Anticipated challenges 357 A.15 Active learning for OCT images annotation: concept 358 A.15.1 Method 358 A.15.1.1 Deep active learning 358 A.15.1.2 OCT data annotation with active learning 359 A.15.2 Anticipated challenges 359 A.16 Life-long learning for optimizing models: concept 360 A.16.1 Method 360 A.16.1.1 Elastic weight consolidation 360 A.16.1.2 Application on multi-tasks learning 361 A.16.2 Anticipated challenges 361 A.17 Open source software, modules, and codes. 362 A.17.1 Open source tools 362 A.17.2 Open source python modules 362 A.17.3 Open source repositories 363 A.18. Reference in appendix 365 | - |
| dc.language.iso | en | - |
| dc.subject | 蘇木精與伊紅染色(H&E) | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 影像去雜訊 | zh_TW |
| dc.subject | 光學同調斷層掃描(OCT) | zh_TW |
| dc.subject | 影像辨識 | zh_TW |
| dc.subject | 可解釋性人工智慧 | zh_TW |
| dc.subject | 影像轉換 | zh_TW |
| dc.subject | explainable AI | en |
| dc.subject | optical coherence tomography (OCT) | en |
| dc.subject | hematoxylin and eosin (H&E) stain | en |
| dc.subject | image segmentation | en |
| dc.subject | image denoising | en |
| dc.subject | image translation | en |
| dc.subject | image classification | en |
| dc.title | 應用深度學習定量和定性分析細胞解析度光學同調斷層掃描影像中的人體皮膚結構和病變 | zh_TW |
| dc.title | Quantitative and Qualitative Analysis of Human Skin Structures and Lesions in Cellular-Resolution OCT Images by Deep Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳育弘;陳宏銘;李翔傑 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Hung Wu;Homer H. Chen;Hsiang-Chieh Lee | en |
| dc.subject.keyword | 光學同調斷層掃描(OCT),蘇木精與伊紅染色(H&E),影像分割,影像去雜訊,影像轉換,影像辨識,可解釋性人工智慧, | zh_TW |
| dc.subject.keyword | optical coherence tomography (OCT),hematoxylin and eosin (H&E) stain,image segmentation,image denoising,image translation,image classification,explainable AI, | en |
| dc.relation.page | 369 | - |
| dc.identifier.doi | 10.6342/NTU202301921 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-07-28 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 光電工程學研究所 | - |
| dc.date.embargo-lift | 2025-08-01 | - |
| 顯示於系所單位： | 光電工程學研究所 | |
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