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基於深度學習之前列腺癌病人的臨床意義分級診斷 = = Grading D...

陳嘉豪

基於深度學習之前列腺癌病人的臨床意義分級診斷 = = Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	基於深度學習之前列腺癌病人的臨床意義分級診斷 =/ 陳嘉豪.
Reminder of title:	Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning /
remainder title:	Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning.
Author:	陳嘉豪
Published:	雲林縣 :國立虎尾科技大學 , : 民113.08.,
Description:	[7], 39面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 郭永明.
Subject:	Water-Fat Ratio. -
Online resource:	電子資源

基於深度學習之前列腺癌病人的臨床意義分級診斷 = = Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning /
陳嘉豪

基於深度學習之前列腺癌病人的臨床意義分級診斷 =Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning /Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning.陳嘉豪. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.08. - [7], 39面 :圖, 表 ;30公分.

指導教授: 郭永明.

碩士論文--國立虎尾科技大學電子工程系碩士班.

含參考書目.

在本研究中，我們針對前列腺癌的臨床風險分級進行了深入探討，特別是透過結合多個深度學習模型 (Resnet34) 來處理不同的影像數據類型。這種方法允許我們從各種影像數據中提取豐富的特徵，並進行更為精確的風險評估。特別地，我們專注於前列腺及其周圍脂肪的影像數據，並且探討棕色脂肪與前列腺癌的關係，這些區域的詳細分析對於準確判定癌症風險具有重要意義。透過結合多個Resnet34模型，我們能夠從各個角度分析影像數據，這種多模態融合策略顯著提高了模型在識別前列腺癌的敏感性和特異性。實驗結果表明，使用這種綜合模型框架能夠有效提升診斷性能，尤其是在處理不平衡數據集時，Youden's index達到0.585，證明了其在臨床應用中的可靠性。此外，我們的方法避免了侵入式檢測手段，大幅減少了患者的身體和心理負擔，同時降低了誤診的風險。非侵入性的診斷方法提供了一種更安全、更人性化的醫療選擇，對於提高患者的生活質量具有重要價值。本研究通過創新地結合多個深度學習模型和精確分析前列腺及其周圍脂肪的影像數據，展示了深度學習技術在前列腺癌風險分級中的強大潛力。.

(平裝)Subjects--Topical Terms:

1451628
Water-Fat Ratio.

基於深度學習之前列腺癌病人的臨床意義分級診斷 = = Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning /
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245 1 0 $a 基於深度學習之前列腺癌病人的臨床意義分級診斷 = $b Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning / $c 陳嘉豪.
246 1 1 $a Grading Diagnosis of Clinical Significance for Prostate Cancer Based on Deep Learning.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.08.
300 $a [7], 39面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 郭永明.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學電子工程系碩士班.
504 $a 含參考書目.
520 3 $a 在本研究中，我們針對前列腺癌的臨床風險分級進行了深入探討，特別是透過結合多個深度學習模型 (Resnet34) 來處理不同的影像數據類型。這種方法允許我們從各種影像數據中提取豐富的特徵，並進行更為精確的風險評估。特別地，我們專注於前列腺及其周圍脂肪的影像數據，並且探討棕色脂肪與前列腺癌的關係，這些區域的詳細分析對於準確判定癌症風險具有重要意義。透過結合多個Resnet34模型，我們能夠從各個角度分析影像數據，這種多模態融合策略顯著提高了模型在識別前列腺癌的敏感性和特異性。實驗結果表明，使用這種綜合模型框架能夠有效提升診斷性能，尤其是在處理不平衡數據集時，Youden's index達到0.585，證明了其在臨床應用中的可靠性。此外，我們的方法避免了侵入式檢測手段，大幅減少了患者的身體和心理負擔，同時降低了誤診的風險。非侵入性的診斷方法提供了一種更安全、更人性化的醫療選擇，對於提高患者的生活質量具有重要價值。本研究通過創新地結合多個深度學習模型和精確分析前列腺及其周圍脂肪的影像數據，展示了深度學習技術在前列腺癌風險分級中的強大潛力。.
520 3 $a In this study, we conducted an in-depth exploration of clinical risk stratification for prostate cancer, particularly through the integration of multiple deep learning models (Resnet34) to process various types of imaging data. This approach allowed us to extract rich features from various imaging data and perform more precise risk assessments. Specifically, we focused on imaging data of the prostate and its surrounding adipose tissue, exploring the relationship between brown fat and prostate cancer. Detailed analysis of these areas is crucial for accurately determining cancer risk. By combining multiple Resnet34 models, we were able to analyze imaging data from various perspectives. This multimodal fusion strategy significantly enhanced the model's sensitivity and specificity in identifying prostate cancer. The experimental results demonstrated that using this integrated model framework could effectively improve diagnostic performance, especially in handling imbalanced datasets, achieving a Youden's index of 0.585, which confirms its reliability in clinical applications. Moreover, our approach avoided invasive testing methods, substantially reducing the physical and psychological burden on patients, while also lowering the risk of misdiagnosis. The non-invasive diagnostic method provides a safer, more humane medical option, which is crucial for enhancing patients' quality of life. This research demonstrates the strong potential of deep learning technologies in prostate cancer risk stratification by innovatively combining multiple deep learning models and accurately analyzing imaging data of the prostate and its surrounding fat..
563 $a (平裝)
650 # 4 $a Water-Fat Ratio. $3 1451628
650 # 4 $a Brown Fat. $3 1451627
650 # 4 $a Deep Learning. $3 1127423
650 # 4 $a ResNet 34. $3 1451626
650 # 4 $a Prostate. $3 1419361
650 # 4 $a 水油比. $3 1451625
650 # 4 $a 棕色脂肪. $3 1451624
650 # 4 $a 深度學習. $3 1127425
650 # 4 $a 前列腺. $3 1451623
856 7 # $u https://handle.ncl.edu.tw/11296/unq7va $z 電子資源 $2 http