Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
Deep learning for cardiac image segmentation: a review
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation
Training deep convolutional neural networks usually requires a large amount of labeled
data. However, it is expensive and time-consuming to annotate data for medical image …
data. However, it is expensive and time-consuming to annotate data for medical image …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
Transclaw u-net: Claw u-net with transformers for medical image segmentation
In recent years, computer-aided diagnosis has become an increasingly popular topic.
Methods based on convolutional neural networks have achieved good performance in …
Methods based on convolutional neural networks have achieved good performance in …
Boundary-weighted domain adaptive neural network for prostate MR image segmentation
Accurate segmentation of the prostate from magnetic resonance (MR) images provides
useful information for prostate cancer diagnosis and treatment. However, automated …
useful information for prostate cancer diagnosis and treatment. However, automated …
Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation
Deep convolutional networks have demonstrated state-of-the-art performance on various
challenging medical image processing tasks. Leveraging images from different modalities …
challenging medical image processing tasks. Leveraging images from different modalities …
A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy
Purpose The purpose of this study was to expedite the contouring process for MRI‐guided
adaptive radiotherapy (MR‐IGART), a convolutional neural network (CNN) deep‐learning …
adaptive radiotherapy (MR‐IGART), a convolutional neural network (CNN) deep‐learning …