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Medical image segmentation with limited supervision: a review of deep network models
Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …
cutting-edge models rely heavily on large-scale annotated training examples, which are …
Deep neural architectures for medical image semantic segmentation
Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration
The main objective of anatomically plausible results for deformable image registration is to
improve model's registration accuracy by minimizing the difference between a pair of fixed …
improve model's registration accuracy by minimizing the difference between a pair of fixed …
A transformed-feature-space data augmentation method for defect segmentation
Data augmentation is widely used in convolutional neural network (CNN) models to improve
the performance of downstream tasks. The images generated by traditional data …
the performance of downstream tasks. The images generated by traditional data …
Self-supervised generative style transfer for one-shot medical image segmentation
In medical image segmentation, supervised deep networks' success comes at the cost of
requiring abundant labeled data. While asking domain experts to annotate only one or a few …
requiring abundant labeled data. While asking domain experts to annotate only one or a few …
One-shot neuroanatomy segmentation through online data augmentation and confidence aware pseudo label
Recently, deep learning-based brain segmentation methods have achieved great success.
However, most approaches focus on supervised segmentation, which requires many high …
However, most approaches focus on supervised segmentation, which requires many high …
One-shot traumatic brain segmentation with adversarial training and uncertainty rectification
Brain segmentation of patients with severe traumatic brain injuries (sTBI) is essential for
clinical treatment, but fully-supervised segmentation is limited by the lack of annotated data …
clinical treatment, but fully-supervised segmentation is limited by the lack of annotated data …
[ספר][B] A Novel Deep Learning-Based Framework for Context-Aware Semantic Segmentation in Medical Imaging
MZ Khan - 2023 - search.proquest.com
Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …