Interactive medical image annotation using improved Attention U-net with compound geodesic distance
Y Zhang, J Chen, X Ma, G Wang, UA Bhatti… - Expert systems with …, 2024 - Elsevier
Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …
planning, and deep learning in the development of medical images. However, creating large …
Going to extremes: weakly supervised medical image segmentation
Medical image annotation is a major hurdle for developing precise and robust machine-
learning models. Annotation is expensive, time-consuming, and often requires expert …
learning models. Annotation is expensive, time-consuming, and often requires expert …
Biomedical image segmentation via representative annotation
Deep learning has been applied successfully to many biomedical image segmentation
tasks. However, due to the diversity and complexity of biomedical image data, manual …
tasks. However, due to the diversity and complexity of biomedical image data, manual …
RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning
Deep-learning algorithms typically fall within the domain of supervised artificial intelligence
and are designed to “learn” from annotated data. Deep-learning models require large …
and are designed to “learn” from annotated data. Deep-learning models require large …
Towards a better understanding of annotation tools for medical imaging: a survey
M Aljabri, M AlAmir, M AlGhamdi… - Multimedia tools and …, 2022 - Springer
Medical imaging refers to several different technologies that are used to view the human
body to diagnose, monitor, or treat medical conditions. It requires significant expertise to …
body to diagnose, monitor, or treat medical conditions. It requires significant expertise to …
Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray
Background and objective Automatic segmentation and annotation of medical image plays a
critical role in scientific research and the medical care community. Automatic segmentation …
critical role in scientific research and the medical care community. Automatic segmentation …
Robust medical image segmentation from non-expert annotations with tri-network
Deep convolutional neural networks (CNNs) have achieved commendable results on a
variety of medical image segmentation tasks. However, CNNs usually require a large …
variety of medical image segmentation tasks. However, CNNs usually require a large …
Diverse data augmentation for learning image segmentation with cross-modality annotations
The dearth of annotated data is a major hurdle in building reliable image segmentation
models. Manual annotation of medical images is tedious, time-consuming, and significantly …
models. Manual annotation of medical images is tedious, time-consuming, and significantly …
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
The splendid success of convolutional neural networks (CNNs) in computer vision is largely
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
End-to-end automatic image annotation based on deep CNN and multi-label data augmentation
X Ke, J Zou, Y Niu - IEEE Transactions on Multimedia, 2019 - ieeexplore.ieee.org
Automatic image annotation is a key step in image retrieval and image understanding. In this
paper, we present an end-to-end automatic image annotation method based on a deep …
paper, we present an end-to-end automatic image annotation method based on a deep …