問題的答案無所不包論文書籍站
AutoML Vision的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包
AutoML Vision的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦寫的 Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence 可以從中找到所需的評價。
另外網站AutoML Vision Edge: Comparing Model Formats - Heartbeat也說明：This is the final post in our series covering the ins and outs of working with Google's AutoML Vision Edge platform.
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AutoML Vision關鍵因素是什麼，來自於Ductal carcinoma in situ (DCIS)、minimal invasive breast cancer、machine learning、ultrasound imaging、mammographic imaging。
而第二篇論文國立中央大學 資訊工程學系 陳慶瀚所指導 劉肇資的 3.5層人工智慧邊緣運算物聯網閘道器及其在步態辨識和行人重識別的應用 （2021），提出因為有 邊緣運算、物聯網、閘道器、步態辨識、行人重識別、人工智慧的重點而找出了 AutoML Vision的解答。
最後網站Google Cloud launches AutoML Tables, Video Intelligence則補充：Object detection (beta) in the full AutoML Vision can identify the position of items within an image, and in context with one another.
Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence
為了解決AutoML Vision 的問題，作者 這樣論述：
George K. Thiruvathukal is a professor of Computer Science at Loyola University Chicago, Illinois, USA. He is also a visiting faculty at Argonne National Laboratory. His research areas include high performance and distributed computing, softwareengineering, and programming languages.Yung-Hsiang Lu i
s a professor of Electrical and Computer Engineering at Purdue University, Indiana, USA. He is the first director of Purdue’s John Martinson Engineering Entrepreneurial Center. He is a fellow of the IEEE and distinguished scientist of the ACM. His research interests include computer vision, mobile s
ystems, and cloud computing.Jaeyoun Kim is a technical program manager at Google, California, USA. He leads AI research projects, including MobileNets and TensorFlow Model Garden, to build state-of-the-art machine learning models and modeling libraries for computer vision and natural language proces
sing.Yiran Chen is a professor of Electrical and Computer Engineering at Duke University, North Carolina, USA. He is a fellow of the ACM and the IEEE. His research areas include new memory and storage systems, machine learning and neuromorphiccomputing, and mobile computing systems.Bo Chen is the Di
rector of AutoML at DJI, Guangdong, China. Before joining DJI, he was a researcher at Google, California, USA. His research interests are the optimization of neural network software and hardware as well as landing AI technology in products with stringent resource constraints.
Machine learning algorithm for classification of Ductal carcinoma in situ and minimal invasive breast cancer
為了解決AutoML Vision 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.
3.5層人工智慧邊緣運算物聯網閘道器及其在步態辨識和行人重識別的應用
為了解決AutoML Vision 的問題，作者劉肇資 這樣論述：
隨著愈來愈多物聯網設備產生的影像資料和進階的影像辨識應用，各種軟體硬體架構不敷所需，還有隨之而來的隱私需求，傳統工業物聯網閘道器所提供的運算資源和架構已經無法符合需求。在跨攝影機的生物識別技術上，需要消耗大量的AI（Artificial Intelligence）運算資源，也有著針對不同應用和規模調整閘道器大小的需求，並且因為隱私和連線穩定性的問題無法連接到雲端。因此，我們需要一個新穎並且可以執行AI應用程式的IoT 閘道器設計。我們提出了3.5層式邊緣運算架構AIoT(AI Internet of Things）邊緣運算（Edge Computing）閘道器架構，這個架構利用了嵌入式硬體以
及微服務架構（Microservice）提供了彈性及可擴展AIoT服務，並且可以容納各種不同的AI硬體和軟體佈局,這是傳統工業物聯網閘道器所無法提供的。最後需要用跨攝影機的生物識別技術作為這個架構的應用驗證,我們選擇了在這個架構上同時執行步態辨識和行人重識別應用。測試結果顯示，我們的3.5層 AIoT EC Gateway，可以隨時調整硬體規模，支援不同的應用架構，也可以採用不同的軟體佈局或是接入異質硬體設備以提供AI加速服務，並且提供比高階AI加速器更好的能效。