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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆 | zh_TW |
| dc.contributor.advisor | Fei-Pei Lai | en |
| dc.contributor.author | 楊雅貽 | zh_TW |
| dc.contributor.author | Ya-Yi Yang | en |
| dc.date.accessioned | 2023-08-01T16:22:44Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-01 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-05 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88009 | - |
| dc.description.abstract | 在現代醫療體系中，主要醫院具有豐富的資源，而分院的資源相對較少。因此，主要醫院會負責訓練深度學習模型，而分院則將其收集的數據提供給主要醫院進行訓練。
本論文的研究目標是在此背景下，探討分院需要累積多少數據，再和主要醫院的數據一起進行訓練，期望在每間參與醫院的測試集上顯著提升模型性能。除了使用燒燙傷的數據集，我們也在交通工具的數據集上模擬了加入不同訓練資料量的情景。除此之外，考慮到主要醫院需要持續接收並訓練新數據，我們的研究目標也嘗試使用綠色學習的架構，運用多種輕量化的模型，旨在接近原始燒燙傷分割模型的結果，同時降低訓練模型時所需的資源和成本。 | zh_TW |
| dc.description.abstract | The main hospital has abundant resources in the modern medical system, while the branch hospitals have relatively few resources. Usually, the main hospital will train the deep learning model, and the branches will provide the data it collects to the main hospital for training.
In this study, our goal is to analyze how much data the branch hospital needs to accumulate and train with the data of the main hospital in this context to significantly improve the model performance on the test dataset of each participating hospital. Besides the burn dataset, we simulated scenarios with varying training data volumes using the transportation dataset. Furthermore, considering that the main hospital needs to continuously receive and train new data, our research goal also attempts to utilize green learning frameworks and various lightweight models. The aim is to approximate the initial burns segmentation model results while reducing the resources and costs required during further model training. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-01T16:22:44Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-01T16:22:44Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書... ...............................i
誌謝............................................iii 中文摘要.........................................iv ABSTRACT.........................................v CONTENTS........................................vi LIST OF FIGURES...............................viii LIST OF TABLES..................................ix Chapter 1 Introduction.....................1 Chapter 2 Related Works....................3 2.1 Incremental Learning.....................3 2.1.1 Task Incremental.........................3 2.1.2 Domain Incremental.......................4 2.1.3 Class Incremental........................5 2.2 Convolutional Neural Network.............5 2.3 Lightweight Architecture.................7 2.4 Green Learning..........................14 2.4.1 Unsupervised Representation Learning....14 2.4.2 Supervised Feature Learning.............16 2.4.3 Supervised Decision Learning............17 2.5 Segmentation............................17 2.5.1 Non-deep-learning-based Methods.........18 2.5.2 Deep-learning-based Methods.............19 2.6 Loss Function...........................20 Chapter 3 Research Methods................22 3.1 Data Processing and Augmentation........22 3.2 Data-Quantity Scenarios.................24 3.3 Green-Learning-based Segmentation.......25 3.4 Deep-Learning-based Segmentation........27 3.4.1 Network Architecture....................28 Chapter 4 Experiment Result...............31 4.1 Dataset.................................31 4.2 Evaluation Metrics......................31 4.3 Diverse Data Quantity Evaluations.......34 4.3.1 Homogeneous dataset.....................34 4.3.2 Heterogeneous dataset...................40 4.3.3 Ablation Study on Heterogeneous Data....44 4.4 Segmentation............................47 4.4.1 GL-based................................47 4.4.2 Limitations and Challenges..............49 4.4.3 DL-based Model Comparison...............52 Chapter 5 Conclusion and Future Work......55 5.1 Conclusion..............................55 5.2 Future Work.............................55 REFERENCE.......................................57 | - |
| dc.language.iso | en | - |
| dc.subject | 增量式學習 | zh_TW |
| dc.subject | U-Net | zh_TW |
| dc.subject | 語義分割 | zh_TW |
| dc.subject | 輕量化神經網路 | zh_TW |
| dc.subject | 燒燙傷傷口辨識 | zh_TW |
| dc.subject | 綠色學習 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | deep learning | en |
| dc.subject | incremental learning | en |
| dc.subject | U-Net | en |
| dc.subject | semantic segmentation | en |
| dc.subject | burn wound recognition | en |
| dc.subject | lightweight neural network | en |
| dc.subject | green learning | en |
| dc.title | 實際情境下訓練數據對模型性能影響的量化分析及輕量級神經網絡的應用 | zh_TW |
| dc.title | Quantitative Analysis of the Impact of Training Data on Model Performance in Real-World Scenarios and the Application of Lightweight Neural Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 張哲瑋;傅楸善;顏廷聿;劉庭祿 | zh_TW |
| dc.contributor.oralexamcommittee | Che-Wei Chang;Chiou-Shann Fuh;Ting-Yu Yan;Ting-Lu Liu | en |
| dc.subject.keyword | 增量式學習,深度學習,綠色學習,燒燙傷傷口辨識,輕量化神經網路,語義分割,U-Net, | zh_TW |
| dc.subject.keyword | incremental learning,deep learning,green learning,burn wound recognition,lightweight neural network,semantic segmentation,U-Net, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202301015 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-07-06 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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