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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Yi-Cheng Yang | en |
| dc.contributor.author | 楊逸崢 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:15:22Z | - |
| dc.date.copyright | 2022-10-19 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-23 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84552 | - |
| dc.description.abstract | 在經常人滿為患的醫院急診中，時間是最重要的資源之一，而為了有效運用黃金時間，判別病患緊急程度的檢傷流程則成為關鍵的一環。要如何在加速檢傷流程的同時維持判斷的準確、客觀一直是困難的課題，因此在本研究中，我們專注在與電子化檢傷相關的任務，期望一個完善的自動化系統最終能提高醫療資源使用的效率。 在台灣急診檢傷流程中，疼痛指數為其中一項重要指標，而人們在大多數情況下會將疼痛反映在臉部表情上，故我們著眼於電腦視覺方法，建立基於臉部影像之深度學習模型。在現行醫療體系下，疼痛指數為病患根據視覺類比表自行評估，然而其數值具有較強的主觀性，且該疼痛指數為序列級別的標籤。在影像分析任務上，相較於逐幀標註的指標，存在資料標註不精確的問題。更具體地說，患者檢傷過程中表情並非維持不變，例如疼痛類型為間歇性疼痛之患者，如何從檢傷影片中找出含有疼痛表現的片段十分關鍵。在我們的實驗中，我們提出了具有事例評價機制的深度學習模型。我們將較短的影片片段稱為事例，並輸入模型，藉由多事例學習訓練模型產生評價分數，以此尋找可能的關鍵畫面，最終改善模型表現與可解釋性。 總結來說，我們將建構一個可用於實際場域的AI輔助系統作為目標，設計了一個支援線上預測的疼痛指數預測系統。 | zh_TW |
| dc.description.abstract | In the emergency department (ED) of hospitals that are often overcrowded, time is one of the most valuable resources. To effectively utilize the golden time, the triage process for estimating the urgency of different patients becomes extremely crucial. Accelerating the triage process while maintaining the accuracy and objectivity of judgment has always been a dilemma. Therefore, in this research, we focus on the tasks related to the automatic triage system and hope that the system can improve the efficiency of utilizing medical resources. Considering pain level is one of the major indicators in the Taiwan triage process, and people usually reflect their pain on their facial expressions, we implement a deep learning model based on facial videos via computer vision methods. In the current medical system, the commonly used pain metric is Visual Analog Score (VAS), which is typically provided through patient self-report. However, VAS is a sequence-level subjective metric. In comparison with frame-level labels, sequence or video-level annotations are more inexact. More specifically, patients' facial expressions may change dramatically during the triage process. As a result, for patients suffering from intermittent pain, recognizing the durations and timings of painful expressions are essential for pain intensity estimation. In this thesis, short video clips are considered as instances and are input to the model. Via our proposed multiple-instance learning approaches, our model learns to appraise the value of instances. Based on the generated instance scores, we improve the performance and interpretability of our pain level assessment model. To sum up, in this thesis we pursue the goal of implementing our system in real clinical situations, so that an online inference framework for pain level estimation is provided. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:15:22Z (GMT). No. of bitstreams: 1 U0001-0109202216125900.pdf: 9802050 bytes, checksum: a5697ff265ade13b403ec4371cd9bafc (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Task Description 1 1.2 Motivation 3 1.3 Related Work 6 1.3.1 Pain Intensity Estimation 7 1.3.2 Multiple-instance Learning 8 1.4 Contribution 9 1.5 Thesis Organization 10 Chapter 2 Preliminaries 12 2.1 Convolutional Neural Network 12 2.1.1 Convolutional Layers 13 2.1.2 Pooling Layers 15 2.1.3 Activation Function 16 2.1.4 Residual Net 17 2.1.5 3D Residual Net 17 2.1.6 Siamese Network 18 2.2 Inexact Supervision 20 2.2.1 Weakly Supervised Learning 20 2.2.2 Multiple Instance Learning 20 2.3 Pain Intensity Metrics 21 2.3.1 Prkachin and Solomon Pain Intensity Scale 22 2.3.2 Visual Analog Scale 23 Chapter 3 MIL Models 25 3.1 System Overview 25 3.2 Network Architecture Design 27 3.2.1 Preprocessing 27 3.2.2 Backbone Network 27 3.3 Multiple Instance Learning 29 3.3.1 Problem Definition 30 3.3.2 Instance Score 30 3.3.3 Label-based Multiple Instance Learning 31 3.3.4 Uncertainty-based Multiple Instance Learning 33 3.3.5 Data sampling via Instance Score Table 35 3.3.6 Combination of LB-MIL and UB-MIL 37 3.4 Local Attention Embedding 38 3.5 Training and Inference 39 Chapter 4 Experiments 41 4.1 Datasets 41 4.1.1 UNBC-McMaster shoulder pain expression archive database 41 4.1.2 NTUH-ED Dataset 44 4.2 Implementation Details 45 4.3 Evaluation Metrics 47 4.4 Post Process via Instance Score 49 4.5 Experimental Result on UNBC-McMaster Dataset 50 4.5.1 Quantitative Results 50 4.5.2 Analysis of Loss Weights 52 4.5.3 Analysis of Multiple Instance Learning Approaches 53 4.5.4 Analysis of different length of instance 54 4.5.5 Analysis of data sampling via Instance Score Table 56 4.5.6 Pain Localization via Instance-level Prediction 58 4.6 Experimental Result on NTUH-ED Dataset 59 4.6.1 The Regression and Classification Loss Approach 59 4.6.2 Ablation Study 60 4.6.3 Pain Localization via Instance-level Prediction 61 4.6.4 Automatic Pain Intensity Estimation System in NTUH 62 4.7 Discussion and Future Work 64 Chapter 5 Conclusion 66 REFERENCE 68 | |
| dc.language.iso | en | |
| dc.title | 可評價事例之深度學習模型應用於臉部連續影像的疼痛指數預估 | zh_TW |
| dc.title | Instance Appraisable Deep Learning Model for Sequence-level Pain Intensity Estimation via Facial Videos | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃建華(Chien-Hua Huang),蔡居霖(Chu-Lin Tsai),張智星(Jyh-Shing Jang),王鈺強(Yu-Chiang Wang) | |
| dc.subject.keyword | 疼痛指數,深度學習,電腦視覺,多事例學習,臉部表情, | zh_TW |
| dc.subject.keyword | Pain Level,Deep learning,Computer Vision,Multiple Instance Learning,Facial Expression, | en |
| dc.relation.page | 78 | |
| dc.identifier.doi | 10.6342/NTU202203069 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-09-23 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| dc.date.embargo-lift | 2025-09-19 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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