Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
Multi-scale self-guided attention for medical image segmentation
Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
Survey on deep learning for radiotherapy
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in
combination with other methods. The planning and delivery of radiotherapy treatment is a …
combination with other methods. The planning and delivery of radiotherapy treatment is a …
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
I Domingues, G Pereira, P Martins, H Duarte… - Artificial Intelligence …, 2020 - Springer
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
[HTML][HTML] Review of deep learning based automatic segmentation for lung cancer radiotherapy
X Liu, KW Li, R Yang, LS Geng - Frontiers in oncology, 2021 - frontiersin.org
Lung cancer is the leading cause of cancer-related mortality for males and females.
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Applications and limitations of machine learning in radiation oncology
Machine learning approaches to problem-solving are growing rapidly within healthcare, and
radiation oncology is no exception. With the burgeoning interest in machine learning comes …
radiation oncology is no exception. With the burgeoning interest in machine learning comes …
3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior …
V Pedoia, B Norman, SN Mehany… - Journal of Magnetic …, 2019 - Wiley Online Library
Background Semiquantitative assessment of MRI plays a central role in musculoskeletal
research; however, in the clinical setting MRI reports often tend to be subjective and …
research; however, in the clinical setting MRI reports often tend to be subjective and …
Automated segmentation of tissues using CT and MRI: a systematic review
Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …
body using computed tomography and magnetic resonance imaging has been rapidly …