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 …
[PDF][PDF] Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review.
J Shao, S Chen, J Zhou, H Zhu, Z Wang… - … -Computer Modeling in …, 2023 - cdn.techscience.cn
As a mainstream research direction in the field of image segmentation, medical image
segmentation plays a key role in the quantification of lesions, three-dimensional …
segmentation plays a key role in the quantification of lesions, three-dimensional …
Automatic detect lung node with deep learning in segmentation and imbalance data labeling
In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is
employed to segment the position of lung nodules, which are an early symptom of lung …
employed to segment the position of lung nodules, which are an early symptom of lung …
UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation
R Hassan, MRH Mondal, SI Ahamed - PloS one, 2024 - journals.plos.org
Organ segmentation has become a preliminary task for computer-aided intervention,
diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from …
diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from …
AI in the loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows
Introduction Methods that automatically flag poor performing predictions are drastically
needed to safely implement machine learning workflows into clinical practice as well as to …
needed to safely implement machine learning workflows into clinical practice as well as to …
Evaluation and Segregation of Fruit Quality using Machine and Deep Learning Techniques
MK Mali, SR Devake, SM Kharpude… - 2022 International …, 2022 - ieeexplore.ieee.org
Identifying fruit fruits is an essential part of fruit plantation smart management. This paper
presents a mechanism based on the available deep learning model to determine the fruit …
presents a mechanism based on the available deep learning model to determine the fruit …
Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation
Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for
automated segmentation methods and can be crucial for downstream radiation treatment …
automated segmentation methods and can be crucial for downstream radiation treatment …
[PDF][PDF] Uncertainty Driven Bottleneck Attention U-net for OAR Segmentation.
Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for
automated segmentation methods and can be crucial for downstream radiation treatment …
automated segmentation methods and can be crucial for downstream radiation treatment …
Adenoid segmentation in X-ray images using U-Net
AA Alshbishiri, MA Marghalani, HA Khan… - 2021 National …, 2021 - ieeexplore.ieee.org
Use of machine learning and specifically deep learning-based techniques for medical
diagnosis has created a significant impact on early and easier diagnosis in the domain of …
diagnosis has created a significant impact on early and easier diagnosis in the domain of …
Multi-channel MRI Embedding: An Effective Strategy for Enhancing Brain Tumor Segmentation
A Pandya, C Samuel, N Patel, V Patel… - 2021 IEEE Applied …, 2021 - ieeexplore.ieee.org
A brain tumor is a collection or a mass of abnormal cells in the brain. One of the most
important diagnostic tasks in medical image processing is the brain whole tumor …
important diagnostic tasks in medical image processing is the brain whole tumor …