Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
A review of deep learning on medical image analysis
Compared with common deep learning methods (eg, convolutional neural networks),
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network
F Ma, T Sun, L Liu, H Jing - Future Generation Computer Systems, 2020 - Elsevier
The prevalence of chronic kidney disease (CKD) increases annually in the present scenario
of research. One of the sources for further therapy is the CKD prediction where the Machine …
of research. One of the sources for further therapy is the CKD prediction where the Machine …
Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations
JMJ Valanarasu, VA Sindagi, I Hacihaliloglu… - … Image Computing and …, 2020 - Springer
Due to its excellent performance, U-Net is the most widely used backbone architecture for
biomedical image segmentation in the recent years. However, in our studies, we observe …
biomedical image segmentation in the recent years. However, in our studies, we observe …
Machine learning in robotic ultrasound imaging: Challenges and perspectives
This article reviews recent advances in intelligent robotic ultrasound imaging systems. We
begin by presenting the commonly employed robotic mechanisms and control techniques in …
begin by presenting the commonly employed robotic mechanisms and control techniques in …
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
[HTML][HTML] Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images
Kidney stone disease is a serious public health concern that is getting worse with changes
in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a …
in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a …
RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation
Breast ultrasound images segmentation is one of the key steps in clinical auxiliary diagnosis
of breast cancer, which seriously threatens women's health. Currently, deep learning …
of breast cancer, which seriously threatens women's health. Currently, deep learning …
Deep segmentation networks for segmenting kidneys and detecting kidney stones in unenhanced abdominal CT images
D Li, C Xiao, 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) …
DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation
G Chen, Y Liu, J Qian, J Zhang, X Yin, L Cui… - Expert Systems with …, 2023 - Elsevier
The automatic and accurate medical ultrasound image segmentation has been a
challenging task due to the coupled interference of various internal and external factors. In …
challenging task due to the coupled interference of various internal and external factors. In …