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研究生: 張博翔
Chang, Po Hsiang
論文名稱: 用於光學相干斷層掃描之基於深度學習和聯邦學習框架之視網膜層分割技術
Retinal layer segmentation technology based on deep learning and federated learning framework for optical coherence tomography
指導教授: 呂成凱
Lu, Cheng-Kai
口試委員: 呂成凱
Lu, Cheng-Kai
連中岳
Lien, Chung-Yueh
林承鴻
Lin, Cheng-Hung
口試日期: 2024/07/15
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 74
中文關鍵詞: 視網膜層深度學習聯邦學習卷積神經網路
英文關鍵詞: Retinal Layer, Deep learning, Federated learning, Convolutional Neural Network
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202401377
論文種類: 學術論文
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  • 在本研究中,我們提出了一種輕量級模型FPENet(α),以FPENet為基底,用於處理專為邊緣設備設計的 OCT 影像中視網膜層分割。視網膜層分割是眼科診斷的重要工具,但其在資源有限的邊緣設備上應用時存在計算成本和精度之間的瓶頸。FedLion(α)在使用 HCMS資料集、NR206資料集及OCT5K資料集進行訓練和測試時,實現了高精度和高效率。該模型經過最佳化,實現了精度和計算成本之間的平衡。FPENet(α)可以有效地捕捉不同尺度的特徵,同時大幅降低計算成本,非常適合部署在如Raspberry Pi等資源有限的邊緣設備上,其輕量化設計使其在計算資源和內存容量方面具有顯著優勢。聯邦學習的部分我們以FedLion為基礎添加了L2正則化與學習率遞減,提出FedLion(α),有效處理數據非獨立同分布的問題。數據顯示使用FPENet(α)與FedLion(α)進行聯邦學習,相較於原先只使用FPENet(α),在HCMS資料集平均DICE係數提升了0.7%,在NR206資料集提升了3.75%,在OCT5K資料集提升了9.1%。

    In this study, we propose a lightweight model, FPENet(α), based on FPENet, designed for retinal layer segmentation in OCT images specifically for edge devices. Retinal layer segmentation is a crucial tool for ophthalmic diagnosis, but it faces the challenge of balancing computational cost and accuracy when applied to resource-constrained edge devices. FedLion(α) achieves high accuracy and efficiency when trained and tested on the HCMS dataset, NR206 dataset, and OCT5K dataset. This model has been optimized to balance precision and computational cost.FPENet(α) can effectively capture multi-scale features while significantly reducing computational cost, making it highly suitable for deployment on resource-limited edge devices such as the Raspberry Pi. Its lightweight design provides significant advantages in terms of computational resources and memory capacity. In the federated learning part, we enhanced FedLion by incorporating L2 regularization and learning rate decay, creating FedLion(α), which effectively addresses the issue of non-IID data.Data shows that using FPENet(α) and FedLion(α) for federated learning, compared to using FPENet(α) alone, increases the average DICE coefficient by 0.7% on the HCMS dataset, by 3.75% on the NR206 dataset, and by 9.1% on the OCT5K dataset.

    誌 謝 i 目 錄 iv 表目錄 vii 圖目錄 viii 第一章 緒論 1 1.1 背景和研究動機 1 1.2 研究目的 2 1.3 論文架構 4 第二章 文獻探討 5 2.1 光學相干斷層掃描 5 2.2 語意分割(Semantic segmentation) 6 2.2.1語意分割模型技術 7 2.2.2 語意分割模型於OCT的近代應用 12 2.3 聯邦學習(Federated Learning, FL) 13 1. 橫向聯邦學習(Horizontal Federated Learning, HFL) 14 2. 縱向聯邦學習(Vertical Federated Learning, VFL) 15 3. 聯邦遷移學習(Federated Transfer Learning, FTL) 15 4. 聯邦強化學習(Federated Reinforcement Learning, FRL) 16 2.3.1 聯邦學習資料的非獨立同分布 16 2.3.2 聯邦學習架構與訓練流程 18 2.3.3 聯邦學習聚合算法 20 2.3.4 聯邦學習平台 23 第三章 研究方法設計 25 3.1 研究流程 25 3.2 資料集 28 3.2.1 HCMS Dataset 28 3.2.2 NR206 Dataset 29 3.2.3 OCT5K Dataset 30 3.3 FPENet網路模型架構 31 3.3.1 Encoder 層 32 3.3.2 Decoder 層 37 3.4 聯邦學習使用架構 40 3.5 評估指標 43 3.5.1 參數量(Parameters) 43 3.5.2 浮點數運算數(FLOPs) 43 3.5.3 運算強度(I) 44 第四章 實驗結果與討論 47 4.1 實驗說明 47 4.1.1 批次處理大小(Batch Size)說明 48 4.1.2 學習率(Learning Rate)說明 49 4.1.3 迭代(Number of Epochs)說明 49 4.1.4 優化器(Optimizer)說明 49 4.1.5 評估指標與損失函數(Loss Function)說明 49 4.2 實驗環境 50 4.3 研究結果 52 4.3.1 運算強度比較 53 4.3.2 HCMS dataset 55 4.3.3 NR206 dataset 57 4.3.4 OCT5K dataset 59 4.4 消融實驗 61 第五章 結論與未來展望 67 5.1 結論 67 5.2 未來展望 68 參考文獻 69 自 傳 73 學術成就 74

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