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

Anwar, Syed Muhammad; Altaf, Tooba; Rafique, Khola; RaviPrakash, Harish; Mohy-ud-Din, Hassan; Bağcı, Ulaş

doi:10.48550/arxiv.1910.07470

Cited by 1 publication

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“…Machine learning, specifically deep learning models, have been successful in a wide range of image analysis tasks. Medical image computing has been benefiting from these advances and resulting in some exciting avenues of research [5], [6]. One of the advantages of using these deep learning algorithms is their ability to learn an effective data representation without the need for pre-identifying the appropriate features in a hand crafted manner.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Musculoskeletal Image Analysis

Irmakci

Anwar

Torigian

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging (MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machine learning, specifically deep learning methods, can be used for rapid and accurate image analysis of MRI scans, an unmet clinical need in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.

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Section: Introductionmentioning

confidence: 99%