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arXiv 1910.07470eess.IVcs.CV

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

University of Central Florida(中佛罗里达大学);University of Engineering and Technology, Taxila(塔克西拉工程技术大学);University of Wah(瓦赫大学);Lahore University of Management Sciences(拉合尔管理科学大学)

Syed Muhammad Anwar, Tooba Altaf, Khola Rafique, Harish RaviPrakash, Hassan Mohy-ud-Din, Ulas Bagci

英文摘要:

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistancein diagnosis of cancer, planning of treatment strategy, and predictionof survival. Radiomics in neuro-oncology has progressed significantly inthe recent past. Deep learning has outperformed conventional machinelearning methods in most image-based applications. Convolutional neu-ral networks (CNNs) have seen some popularity in radiomics, since theydo not require hand-crafted features and can automatically extract fea-tures during the learning process. In this regard, it is observed that CNNbased radiomics could provide state-of-the-art results in neuro-oncology,similar to the recent success of such methods in a wide spectrum ofmedical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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