[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Radiomics in renal cell carcinoma—a systematic review and meta-analysis
J Mühlbauer, L Egen, KF Kowalewski, M Grilli… - Cancers, 2021 - mdpi.com
Simple Summary Radiomics may answer questions where the conventional interpretation of
medical imaging has limitations. The aim of our systematic review and meta-analysis was to …
medical imaging has limitations. The aim of our systematic review and meta-analysis was to …
Vascular implications of COVID-19: role of radiological imaging, artificial intelligence, and tissue characterization: a special report
The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people
worldwide, with mortality exceeding six million. The average survival span is just 14 days …
worldwide, with mortality exceeding six million. The average survival span is just 14 days …
U-Net-based models towards optimal MR brain image segmentation
Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …
therefore, an automatic and generalized system to address this task is needed. Among all …
Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion
Objectives To develop a deep learning-based method for automated classification of renal
cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed …
cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed …
UNet deep learning architecture for segmentation of vascular and non-vascular images: a microscopic look at UNet components buffered with pruning, explainable …
Biomedical image segmentation (BIS) task is challenging due to the variations in organ
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …
A systematic review of the automatic kidney segmentation methods in abdominal images
M Pandey, A Gupta - Biocybernetics and Biomedical Engineering, 2021 - Elsevier
Abstract Background and Purpose The precise kidney segmentation is very helpful for
diagnosis and treatment planning in urology, by giving information about malformation in the …
diagnosis and treatment planning in urology, by giving information about malformation in the …
Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse‐to‐fine convolutional neural network
Purpose Brachytherapy combined with external beam radiotherapy (EBRT) is the standard
treatment for cervical cancer and has been shown to improve overall survival rates …
treatment for cervical cancer and has been shown to improve overall survival rates …
The application and development of deep learning in radiotherapy: A systematic review
D Huang, H Bai, L Wang, Y Hou, L Li… - … in Cancer Research …, 2021 - journals.sagepub.com
With the massive use of computers, the growth and explosion of data has greatly promoted
the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such …
the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such …
3D multi-scale residual fully convolutional neural network for segmentation of extremely large-sized kidney tumor
Background and objective We propose a novel deep neural network, the 3D Multi-Scale
Residual Fully Convolutional Neural Network (3D-MS-RFCNN) to improve segmentation in …
Residual Fully Convolutional Neural Network (3D-MS-RFCNN) to improve segmentation in …