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Titlebook: Artificial Intelligence in Cardiothoracic Imaging; Carlo N. De Cecco,Marly van Assen,Tim Leiner Book 2022 The Editor(s) (if applicable) an

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21#
發(fā)表于 2025-3-25 07:07:17 | 只看該作者
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發(fā)表于 2025-3-25 08:02:58 | 只看該作者
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發(fā)表于 2025-3-25 19:36:17 | 只看該作者
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發(fā)表于 2025-3-25 22:16:20 | 只看該作者
Fallsammlung zum Strafprozessrechtology artificial intelligence. Deep learning methods have led to many exciting breakthroughs in radiological image analysis, and having an understanding of what these methods entail can benefit the interested radiologist.
26#
發(fā)表于 2025-3-26 03:20:17 | 只看該作者
Wer zuerst kommt, mahlt zuerst,ystems. The training of machine learning algorithms requires enormous amounts of data, and structured reports can be a great source for data mining. Structured reporting is an invaluable platform for the emerging integration of artificial intelligence (AI) in medical imaging.
27#
發(fā)表于 2025-3-26 05:54:44 | 只看該作者
,Die Gro?stadt als Ort der Apokalypse,quisition and reconstruction. We discuss these new challenges and provide insights in advanced topics and future opportunities. Throughout the chapter, we outline important applications of deep learning enhancement and reconstruction for static and dynamic applications.
28#
發(fā)表于 2025-3-26 09:10:58 | 只看該作者
Demystifying Artificial Intelligence Technology in Cardiothoracic Imaging: The Essentialsology artificial intelligence. Deep learning methods have led to many exciting breakthroughs in radiological image analysis, and having an understanding of what these methods entail can benefit the interested radiologist.
29#
發(fā)表于 2025-3-26 13:17:05 | 只看該作者
Structured Reporting in Medical Imaging: The Role of Artificial Intelligenceystems. The training of machine learning algorithms requires enormous amounts of data, and structured reports can be a great source for data mining. Structured reporting is an invaluable platform for the emerging integration of artificial intelligence (AI) in medical imaging.
30#
發(fā)表于 2025-3-26 19:45:19 | 只看該作者
Artificial Intelligence for Image Enhancement and Reconstruction in Magnetic Resonance Imagingquisition and reconstruction. We discuss these new challenges and provide insights in advanced topics and future opportunities. Throughout the chapter, we outline important applications of deep learning enhancement and reconstruction for static and dynamic applications.
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