Abstract
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical 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.
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Anwar, S.M., Altaf, T., Rafique, K., RaviPrakash, H., Mohy-ud-Din, H., Bagci, U. (2020). A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology. In: Mohy-ud-Din, H., Rathore, S. (eds) Radiomics and Radiogenomics in Neuro-oncology. RNO-AI 2019. Lecture Notes in Computer Science(), vol 11991. Springer, Cham. https://doi.org/10.1007/978-3-030-40124-5_3
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