Data augmentation for medical imaging: A systematic literature review

F Garcea, A Serra, F Lamberti, L Morra - Computers in Biology and …, 2023 - Elsevier
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 …

A review of deep learning on medical image analysis

J Wang, H Zhu, SH Wang, YD Zhang - Mobile Networks and Applications, 2021 - Springer
Compared with common deep learning methods (eg, convolutional neural networks),
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 …

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 …

Machine learning in robotic ultrasound imaging: Challenges and perspectives

Y Bi, Z Jiang, F Duelmer, D Huang… - Annual Review of …, 2024 - annualreviews.org
This article reviews recent advances in intelligent robotic ultrasound imaging systems. We
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

G Chen, Y Dai, J Zhang - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objective Breast lesions segmentation is an important step of computer-
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

KK Patro, JP Allam, BC Neelapu, R Tadeusiewicz… - Information …, 2023 - Elsevier
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 …

RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation

G Chen, Y Dai, J Zhang - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
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 …

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) …

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 …