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運用深度學習於膝關節損傷核磁共振影像之人工智慧偵測與診斷模型

為了解決Le point 2521的問題，作者TRUONG NGUYEN KHANH HUNG 這樣論述：

Introduction: Efficient and accurate detection is vital for the diagnosis and treatment of knee injuries. In recent years, there is an increase in interest in deep learning (DL) approaches to detecting knee injuries in magnetic resonance imaging (MRI). Studies have shown that DL models are capable

of reaching the same level as human radiologists when it comes to sensitivity and specificity, while at the same time requiring significantly less training time. Current Artificial Intelligent (AI) - based systems are, however, still limited by many different factors, such as unbalanced classes in t

raining data, or the nature of these systems which makes false positives and false negatives almost an inevitability. There are multiple routes for improving upon the existing DL knee injury detection models. As they continue to become more and more advanced, it is expected that the use of these sys

tems will become more popular in the future.Method: In this study, we create multi models based on machine learning (ML) and DL algorithms to perform classification, recognition, and segmentation tasks on knee MRI. In which the two most important components in the knee joint in this study are the an

terior cruciate ligament (ACL) and meniscus.The first model, based on the DenseNet 121 neural network structure, was used to classify images with or without ACL injury. The dataset includes 799 knee MRI reports from Cho Ray Hospital (Vietnam). These MRI data were obtained from previous work in the h

ospital, containing knee MRI reports from 5 years (January 1st, 2015 – December 31st, 2019)Using the Faster-region convolutional neural network (Faster - RCNN) and several convolutional neural networks (CNN) backbone tests, such as VGG-16, Res-Net50, DenseNet-121, EfficientNet-B0, and EfficientNetV2

- B0 algorithms, the second group of models can recognize the ACL on knee MRI as a function of the typical imaging characteristics. This research collected 256 knee MRI examinations performed at Cho Ray Hospital, Ho Chi Minh City, Vietnam, between January 1, 2018, and December 31, 2020 (including t

raining and testing datasets).The third model focuses on automatic identification and classification of meniscus based on the Yolo-v4 object detection model. At the same time, the lesion location is also shown on images by the GRAD-CAM technique. The total number of subjects used in this study was 7

04 patients, including meniscus lesions and the control group. All MRIs in this study were collected before the surgery, and all had no prior surgical history. The MRI scanner at Cho Ray Hospital is MAGNETOM Skyra 3T (Siemen), and at Hoan My Hospital is 3.0T MRI Scanners SIGNA (GE Healthcare). In ad

dition, we also used a public dataset - MRNet dataset (validation dataset) from Stanford University Medical Center with 120 examinations for external testing.Results: The area under the ROC curve (AUC) for the ACL injury classification system was 80.63% with the axial plane and around 78% with both

the sagittal and coronal planes, respectively. All sensitivity and specificity point estimates of the proposed ACL injury detection system were all over 96%, indicating no significant differences in diagnostic performance between different planes.Our DL model detected meniscus tears with 91.4% accur

acy on the internal testing dataset, 89.2% accuracy on the external validation dataset, and 79.9% accuracy on the MRNet dataset, respectively. The meniscus tears were visualized by auto-enlarging the detection area and Grad-CAM images.Conclusion: This report describes the various approaches in knee

MRI experiments to provide different AI models for the prediction of knee injuries. The CNN model applied to classify injured ACL images had high sensitivity and specificity, showing that using a simple structured 2D-CNN is more effective for small datasets and can assist non-experts in assessing th

e assessment of ACL injuries. The proposed model applied to detect meniscus lesions had high accuracy and specificity, showing that our model can assist non-experts in assessing the assessment of meniscus injuries.

超穎材料結構之光學應用研究

為了解決Le point 2521的問題，作者陳則安 這樣論述：

近年來，超穎材料被廣泛的運用在光電領域。藉由選用的材料以及經過設計的次波長週期結構，可精準的調控電磁波的偏振、振幅與相位等特性。隨著微影製程技術的成熟，無塵室製程可以輕易地製作出奈米元件，這也增廣了超材料的應用，例如完美吸收體、超穎透鏡、全像投影等等。第一個研究主題是利用電漿子能帶理論解析之雙曲超材料，雙曲超材料具有特別的電磁特性，可實現廣泛的應用，例如超分辨率和自發輻射。在預測和分析雙曲超材料的這些光學特性時，大多數研究人員採用有效介質理論。然而，該理論僅適用於長波長和無限堆疊層。為了探測表面狀態並驗證光學拓撲轉變，我們製作了由MgF2/Ag 交互堆疊而成的多層膜雙曲超材料。我們所有的分析

、數值計算和實驗測量表明，多層膜雙曲超材料的表面態色散曲線上的“過渡點” 僅取決於金屬層和介電層的厚度比。這的預測結果較傳統的有效介質理論準確。電漿子能帶理論的結果提供了更準確的預測，並可利用於的雙曲超材料的應用。第二個研究主題是超寬頻類黑體完美吸收體，過去幾年，電漿子共振的完美吸收體廣泛的運用在各種應用，例如生物感測器、非線性光學、濾波器和熱發射器。大多數的電漿子完美吸收體都是藉由電子束微影所製作而成，然而，電子束微影成本高昂，限制了這些設備的大規模生產和實際應用性。因此，我們提出了一個多層膜的結構，不需要藉由微影製程就可製作的結構。此多層膜可以同時再横磁模式以及横電模式近乎完美吸收波長90

0奈米到1900奈米的光。此外，此結構對於入射角度有很高的容忍度，在入射角小於70度時，可以吸收80%以上的電磁波。換句話說，我們成功的開發出一個寬頻且對於任意極化角以及任意入射角均可以完美吸收的類黑體完美吸收體。第三個研究主題是惠更斯超穎平面光偏折元件，在這項工作中，我們提出了由二氧化鈦奈米圓盤陣列組成的超穎介面。首先，材料的選擇，二氧化鈦有優異的高介電常數 (εreal≅ 6) 和低損耗 (εimag≅ 0)適合運用在此工作波段。藉由重疊奈米圓盤的電和磁共振以同時實現100%的穿透以及完整的 2π 相位。在本論文中，超穎介面構成的惠更斯模擬達近90%的穿透率。在實驗上，惠更斯超穎介面來穿透

率和以及光偏折效率，分別為 80% 和 15%。我們相信，所提議的惠更斯超穎平面光偏折元件將成為平面光學設備的新典範，包括超透鏡、全息和光束整形設備。