韓劇電影你都看哪一部呢？論文書籍站
Bi rads 4的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列線上看、影評和彩蛋懶人包
Bi rads 4的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦羅崇銘寫的 人工智慧與影像知識詮釋化(修訂版) 和羅崇銘的 人工智慧與影像知識詮釋化都 可以從中找到所需的評價。
另外網站乳房攝影的解讀系統，BI-RADS也說明：乳房攝影的解讀系統，BI-RADS · 1：正常。 · 2：良性發現。 · 3：可能為良性發現。 · 4：懷疑異常。 · 5：高度懷疑為惡性腫瘤。 · 6：切片已證實為惡性腫瘤。
這兩本書分別來自元華文創股份有限公司 和元華文創股份有限公司所出版 。
國立清華大學 生醫工程與環境科學系 彭旭霞所指導 賴志慶的 前列腺癌核磁共振影像的直方圖分析與深度卷積神經網絡 （2021），提出Bi rads 4關鍵因素是什麼，來自於前列腺癌、直方圖分析、核磁共振影像、深度卷積神經網絡、影像分割、動態對比增強影像、多參數核磁共振影像、擴散加權影像、T2加權影像。
而第二篇論文臺北醫學大學 國際醫學研究碩士學位學程 陳榮邦所指導 VU PHAM THAO VY的 Machine learning algorithm for classification of Ductal carcinoma in situ and minimal invasive breast cancer （2021），提出因為有 Ductal carcinoma in situ (DCIS)、minimal invasive breast cancer、machine learning、ultrasound imaging、mammographic imaging的重點而找出了 Bi rads 4的解答。
最後網站Evaluation of the positive predictive value (PPV3) of ACR ...則補充：BI-RADS category 5 is reserved for cancer-like lesions on diagnostic imaging. BI-RADS 4 includes suspicious lesions with some likelihood of malignancy [3,4].
人工智慧與影像知識詮釋化(修訂版)
為了解決Bi rads 4 的問題，作者羅崇銘 這樣論述：
本書以資訊數位化出發，跨領域整合醫學影像資訊與圖書資訊，尤其分類與詮釋資料的描述，是資料科學時代相當重要的一環，利用人工智慧進行醫學知識的分類、利用影像特徵的擷取完成影像詮釋資料的建立，並將日益重要的醫學影像的類型加以整理，包括檔案的形成、儲存容量、存取機制、安全性、使用規範、完整性與標註需求，進行鉅細靡遺地闡述，此概念之延伸將有助詮釋與人類生活息息相關的各種影像資料，以完整詮釋建立永恆的知識。 本書特色 醫學影像已是人工智慧醫學的主戰場， 為此建立醫學影像檔案學的知識整理， 也是圖書資訊在人工智慧時代的重要角色之一。
前列腺癌核磁共振影像的直方圖分析與深度卷積神經網絡
為了解決Bi rads 4 的問題，作者賴志慶 這樣論述：
隨著多參數核磁共振影像(mp-MRI)的發展，對於診斷前列腺癌的準確性有所提高，完整的mp-MRI影像包括T2加權影像(T2W)、擴散加權影像(DWI)、擴散係數影像(ADC)和動態對比增強影像(DCE-MRI)。在本論文中，首先利用直方圖參數來分析DCE-MRI影像中對比劑最大洗入斜率(MWS)和延遲洗出斜率(DPS)於攝護腺腫瘤組織與正常組織的區分能力。結果顯示，在攝護腺的過度區，變異係數唯一在MWS中可以區分腫瘤與正常組織的參數，而在DPS中有較多的參數可以區分腫瘤(如 平均值、偏度、10%、25%、50%、75%和90%)。攝護腺外圍區的部分，在MWS分析中，除了峰度和偏度外，其他直
方圖參數對於區分組織，都有顯著的差異。在DPS中有顯著差異的參數為標準差、四分位距、修正過半峰全寬，百分位數距(90%-10%)。在攝護腺的過度區與外圍區的MWS 和 DPS中，有不同的直方圖參數可以被用來區分腫瘤。最後本論文使用以SegNet為基礎的深度卷積神經網路模型來自動分割攝護腺過度區、外圍區和腫瘤的區域。本文中使用PROSTATEx的數據集中的影像來訓練模型，將三種不同序列的核磁共振影像，分別為T2W、DWI和ADC等三種影像，並將其結合為一張三通道(RGB)的影像，並在影像上圈選出三個區域的範圍，最後利用上述三種影像做不同排列的分組來放入模型訓練。結果發現，在T2W + DWI +
ADC影像組合表現出較好的分割效能，而在個別區域分割的相似係數分別為過度區(90.45%)、外圍區(70.04%)和腫瘤區(52.73%)，使用深度卷積神經網路模型來診斷腫瘤以及分割區域是相當有潛力的。本文的結論為，對於DCE-MRI 時間強度曲線進行直方圖分析，可以用來區分 過度區與外圍區中的良性前列腺組織和腫瘤，並且深度卷積神經模型於影像序列分析中，用來輔助診斷腫瘤有相當大的潛力。
人工智慧與影像知識詮釋化
為了解決Bi rads 4 的問題，作者羅崇銘 這樣論述：
本書以資訊數位化出發，跨領域整合醫學影像資訊與圖書資訊，尤其分類與詮釋資料的描述，是資料科學時代相當重要的一環，利用人工智慧進行醫學知識的分類、利用影像特徵的擷取完成影像詮釋資料的建立，並將日益重要的醫學影像的類型加以整理，包括檔案的形成、儲存容量、存取機制、安全性、使用規範、完整性與標註需求，進行鉅細靡遺地闡述，此概念之延伸將有助詮釋與人類生活息息相關的各種影像資料，以完整詮釋建立永恆的知識。 本書特色 醫學影像已是人工智慧醫學的主戰場， 為此建立醫學影像檔案學的知識整理， 也是圖書資訊在人工智慧時代的重要角色之一。
Machine learning algorithm for classification of Ductal carcinoma in situ and minimal invasive breast cancer
為了解決Bi rads 4 的問題，作者VU PHAM THAO VY 這樣論述：
Introduction: Breast cancer nowadays is the second common cancer in the world and the most common cancer among women, excluding nonmelanoma skin cancers. Breast cancer is not just one disease, it has different types and subtypes that depend on the affected specific cell in the breast. Cancer can be
classified into two types according to whether it has spread: non-invasive and invasive breast cancer. The most frequent kind of non-invasive breast cancer is ductal carcinoma in situ (DCIS). DCIS is cancer that starts in a duct and has not spread into any surrounding breast tissue. Some DCIS patie
nts will not develop the invasive disease, and this has been suggested as a risk of screening mammography. Breast cancers that are invasive have grown outside of the ducts or lobules into the surrounding tissue. As size of the tumor decreases, patients with invasive breast cancer have a better chanc
e of surviving. Despite the prognostic factors, a small percentage of patients with invasive tumors of 10 mm or less (T1a and T1b) die from their cancer. Many studies have been conducted examining traditional histopathological characteristics, including lymph node status, tumor size, histological gr
ade, margin width, and many other biological markers of prognosis. The use of these prognostic factors, while appealing in principle and effective in larger tumors, presents challenges in small tumors. The identification of breast cancer types at an early stage enables patients to choose less invasi
ve treatment options. The purpose of our study was to develop a machine-learning classification model to differentiate DCIS and minimal invasive cancer using clinical characteristics, mammography findings, ultrasound findings and histopathology features.Method: Clinical data, mammography findings an
d ultrasound findings of 420 biopsy-confirmed breast cancer cases were analyzed retrospectively to diagnose DCIS and minimal invasive cancer. The subtypes were categorized based on the histopathology and size of lesion on histological assessment. Four groups of features including clinical data, mamm
ography findings, ultrasound findings and histology findings are used for classification by machine learning. The machine learning techniques used in this study include XGboost, Random Forest, Single Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, and Decision Tree Classifier. To classify
two types of breast cancer, we mainly focus on the XGBoost algorithm trained on clinical characteristics, mammography (MMG) findings, ultrasound (US) findings, and histopathology features that are associated with DCIS and minimal invasive breast cancers. The study used the area under the receiver op
erating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score as measures of model performance. Additionally, this research determined the importance of features by using XGboost and SHapley Additive Explanations (SHAP).Results: The results of this model were valida
ted in 378 women and tested in 42 women (mean age, 58.8 years ± 12.2). The model has high classified performance when combining features importance, with the highest accuracy reaching 0.84 (95% confidence interval [CI]: 0.77, 0.90), an AUC of 0.93 (95% CI: 0.86, 0.96), with the specificity of 0.73 (
95% CI: 0.64, 0.82) and sensitivity of 0.91 (95% CI: 0.73, 0.95). The five most important features illustrated by XGBoost were the presence of calcification on MMG, the existence of lymph node, the presence of microcalcification on histopathology, the shape of the mass on US image, and the orientati
on of mass on US image, and the orientation of mass on US image.Conclusion: XGBoost model combining clinical characteristics, mammography findings, ultrasound findings, and histopathology features, can be applied to classify breast cancer at a level equivalent to radiologists and has the potential t
o detect early invasive breast cancer.