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• W3204333372 startingPage "75" @default.
• W3204333372 abstract "There is a growing recognition that imaging data contain hidden information, sometimes in plain sight. Accessing these data and linking them to clinically relevant information require advancements and fundamental shifts in how we analyze and interpret imaging studies. Artificial intelligence (AI) techniques can improve the process of collecting and cataloguing imaging data of bone tumors, integrating imaging results with other clinical information, recommending personalized treatments, and providing guidance on appropriate follow-up studies. Deep convolutional neural networks (deep-CNNs) are being developed for increasing image quality, detection of bone tumors, differentiation of benign and malignant lesions, and therapy planning. Two main bottlenecks for successful AI applications for bone tumor imaging to date are the need for large datasets and limited computational power, such as appropriate graphic processing units and memory capacity. Currently, deep-CNNs cannot guarantee correct diagnoses in novel circumstances that may not resemble previous training data. However, this problem will likely be solved in the near future by approaches such as cross-training CNNs, enabling AI to learn from sparse data, and sending CNN algorithms to multiple institutions for training purposes rather than collecting multi-institutional data for centralized training. The imaging community needs to join forces to fulfill this important requirement for the rapidly growing field of AI research." @default.
• W3204333372 created "2021-10-11" @default.
• W3204333372 creator A5024195324 @default.
• W3204333372 creator A5034343465 @default.
• W3204333372 creator A5086834693 @default.
• W3204333372 date "2022-01-01" @default.
• W3204333372 modified "2023-09-25" @default.
• W3204333372 title "Artificial intelligence for bone cancer imaging" @default.
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