Anatomy-aided deep learning for medical image segmentation: a review
Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …
years. However, despite these advances, there are still problems for which DL-based …
Box-supervised instance segmentation with level set evolution
In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised
instance segmentation takes advantage of the simple box annotations, which has recently …
instance segmentation takes advantage of the simple box annotations, which has recently …
Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives
The rapid development of artificial intelligence (AI) has gained importance, with many tools
already entering our daily lives. The medical field of radiation oncology is also subject to this …
already entering our daily lives. The medical field of radiation oncology is also subject to this …
Scnet: Training inference sample consistency for instance segmentation
Cascaded architectures have brought significant performance improvement in object
detection and instance segmentation. However, there are lingering issues regarding the …
detection and instance segmentation. However, there are lingering issues regarding the …
Box2mask: Box-supervised instance segmentation via level-set evolution
In contrast to fully supervised methods using pixel-wise mask labels, box-supervised
instance segmentation takes advantage of simple box annotations, which has recently …
instance segmentation takes advantage of simple box annotations, which has recently …
Mgnet: Monocular geometric scene understanding for autonomous driving
We introduce MGNet, a multi-task framework for monocular geometric scene understanding.
We define monocular geometric scene understanding as the combination of two known …
We define monocular geometric scene understanding as the combination of two known …
Scaling wide residual networks for panoptic segmentation
The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the
Residual Networks (ResNets) by stacking a small number of residual blocks with large …
Residual Networks (ResNets) by stacking a small number of residual blocks with large …
Bayesian prompt learning for image-language model generalization
Foundational image-language models have generated considerable interest due to their
efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of …
efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of …
[Retracted] Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network
M Arif, FM Philip, F Ajesh, D Izdrui… - Journal of …, 2022 - Wiley Online Library
One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance
between different skin lesions such as melanoma and nevus in the skin colour images …
between different skin lesions such as melanoma and nevus in the skin colour images …
Efficient Task-specific Feature Re-fusion for More Accurate Object Detection and Instance Segmentation
Feature pyramid representations have been widely adopted in the object detection literature
for better handling of variations in scale, which provide abundant information from various …
for better handling of variations in scale, which provide abundant information from various …