International journal of computer assisted radiology and surgery
Sep 22, 2020
PURPOSE: Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital featur...
In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET ...
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed sin...
Medical & biological engineering & computing
Sep 21, 2020
Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details co...
Proceedings of the National Academy of Sciences of the United States of America
Sep 21, 2020
Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection...
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on s...
Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Sep 18, 2020
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially availab...
Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connectio...
Neuroimaging clinics of North America
Sep 18, 2020
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogene...
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