博碩士論文 111522601 詳細資訊




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姓名 本尼(Brahma reddy Akumalla)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 提升乳癌篩檢效率之批次排程框架
(A Batch Scheduling Framework for Improved Diagnostic Efficiency in Breast Cancer Screening)
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摘要(中) 深度學習在醫學診斷中的應用,特別是對覆雜的腫塊型乳腺癌,取得了顯著進展。一種采用批量調度器進行動態批量大小調整的新型訓練策略顯示出4\%的性能提升。通過結合注意力機制和雙優化器策略,進一步提高了F1分數和性能的強健性,與傳統方法相比表現更優。此外,一種結合卷積層和變壓器模型的新型特征選擇機制在實驗中表現優於現有的注意力機制。這一創新,加上集成了預訓練權重的“ConvNext Tiny”模型,大大增強了乳腺癌檢測系統的強健性,突顯了這些方法在改進醫學診斷程序中的潛力。
摘要(英) The application of deep learning in medical diagnostics, particularly for complex mass-type breast cancers, has seen significant advancements. A novel training strategy that employs a batch scheduler for dynamic batch size adjustment has demonstrated a performance improvement of 4\%. Further enhancements were achieved by incorporating attention mechanisms and dual optimizer strategies, resulting in superior F1 scores and robust performance compared to traditional methods.Additionally, a novel feature selection mechanism combining convolution layers and a transformer model outperformed established attention mechanisms in experimental trials. This innovation, along with the integration of the ′ConvNext Tiny′ model with pre-trained weights, substantially enhanced the robustness of the breast cancer detection system, underscoring the potential of these methodologies for improving medical diagnostic procedures.
關鍵字(中) ★ ConvNext
★ 變壓器
★ 卷積層
★ 注意力
★ 乳房腫塊
關鍵字(英) ★ ConvNext
★ transformer
★ convolution layers
★ attention
★ Breast mass
論文目次 摘要 i
Abstract ii
Acknowledgements iii
List of Contents v
List of Figures vi
List of Tables viii
List of Algorithms ix
Chapter I: Introduction 1
Chapter II: Related works 6
Chapter III: Preliminary 10
3.1 Attention 10
3.1.1 Attention Mechanisms in Deep Learning 10
3.1.2 Triplet Attention: Capturing Cross-Dimensional Interactions 11
3.1.3 Architecture and Implementation 11
3.1.4 Performance Gains and Practical Applications 11
3.1.5 The Future of Triplet Attention 12
3.2 Batch size scheduler 12
3.3 Data Fetecher 14
3.4 Cleaning Data 15
3.5 Convolution Layers 16
3.6 Transformer 17
3.7 Other important things 19
3.7.1 Drop out Layer 19
3.7.2 Pooling layer 20
3.7.3 Activation Layer 21
iv
3.7.4 optimizer 23
3.7.5 Learning rate Scheduler 24
Chapter IV: Methodology 26
4.1 Overview of Classifiers 26
4.1.1 VGG19 26
4.1.2 ResNet 27
4.1.3 EfficientNet 28
4.1.4 ConvNext 30
4.2 The Proposed Method 32
4.3 Tripplet Attention integration with ConvNext Model 34
4.4 Dual optimizer Training 35
4.5 Feature aggregation with Transformer and Convoluation Layers 36
Chapter V: Results 38
5.1 Model Selection 38
5.2 Experiment with rsna weights and Batch scheduler 39
5.3 Experiment with attention 39
5.4 Experiments with dual optimizers 41
5.5 Experiment results with transformer 42
Chapter VI: Implementation details 43
6.1 Imaging Datasets 43
6.2 Processing 45
6.3 Metrics 46
6.3.1 Accuracy 47
6.3.2 F1 Score 47
6.3.3 Confusion Matrix 47
6.4 Experiment Setting 48
Chapter VII: Conclusion and Future Work 49
7.1 Findings 49
7.2 Limits and Directions 50
Bibliography 51
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指導教授 王家慶 莊永裕(Jia-Ching Wang YungYu Zhuang) 審核日期 2024-8-1
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