U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
A review on the use of deep learning for medical images segmentation
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …
images. They have been used extensively for medical image segmentation as the first and …
COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet
Background Currently, there is an urgent need for efficient tools to assess the diagnosis of
COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling …
COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling …
Radiology imaging scans for early diagnosis of kidney tumors: a review of data analytics-based machine learning and deep learning approaches
Plenty of disease types exist in world communities that can be explained by humans'
lifestyles or the economic, social, genetic, and other factors of the country of residence …
lifestyles or the economic, social, genetic, and other factors of the country of residence …
An observational investigation of spatiotemporally contiguous heatwaves in China from a 3D perspective
Understanding the evolution of heatwaves is important for their prediction, mitigation, and
adaptation. While most studies focused on either their temporal variability at individual …
adaptation. While most studies focused on either their temporal variability at individual …
Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI
are providing promising results, leading towards a revolution in the radiologists' workflow …
are providing promising results, leading towards a revolution in the radiologists' workflow …
Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography
In 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people
died from cancer in the United States. Preoperative multi-phase abdominal computed …
died from cancer in the United States. Preoperative multi-phase abdominal computed …
Kidney tumor semantic segmentation using deep learning: A survey of state-of-the-art
A Abdelrahman, S Viriri - Journal of imaging, 2022 - mdpi.com
Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
procedures for early detection and diagnosis are crucial. Some difficulties with manual …
Kidney and renal tumor segmentation using a hybrid V-Net-Based model
Kidney tumors represent a type of cancer that people of advanced age are more likely to
develop. For this reason, it is important to exercise caution and provide diagnostic tests in …
develop. For this reason, it is important to exercise caution and provide diagnostic tests in …
Deep segmentation networks for segmenting kidneys and detecting kidney stones in unenhanced abdominal CT images
D Li, C **ao, Y Liu, Z Chen, H Hassan, L Su, J Liu, H Li… - Diagnostics, 2022 - mdpi.com
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection,
and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) …
and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) …