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FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis
Utilizing multi-modal neuroimaging data is proven to be effective in investigating human
cognitive activities and certain pathologies. However, it is not practical to obtain the full set of …
cognitive activities and certain pathologies. However, it is not practical to obtain the full set of …
FPL+: Filtered pseudo label-based unsupervised cross-modality adaptation for 3D medical image segmentation
Adapting a medical image segmentation model to a new domain is important for improving
its cross-domain transferability, and due to the expensive annotation process, Unsupervised …
its cross-domain transferability, and due to the expensive annotation process, Unsupervised …
Dissecting self-supervised learning methods for surgical computer vision
The field of surgical computer vision has undergone considerable breakthroughs in recent
years with the rising popularity of deep neural network-based methods. However, standard …
years with the rising popularity of deep neural network-based methods. However, standard …
Cross-modality neuroimage synthesis: a survey
Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues
with anatomical properties. The existence of completely aligned and paired multi-modality …
with anatomical properties. The existence of completely aligned and paired multi-modality …
Deep-learning-based methods of attenuation correction for SPECT and PET
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of
single-photon emission computed tomography (SPECT) and positron emission tomography …
single-photon emission computed tomography (SPECT) and positron emission tomography …
Carotid vessel wall segmentation through domain aligner, topological learning, and segment anything model for sparse annotation in mr images
Medical image analysis poses significant challenges due to limited availability of clinical
data, which is crucial for training accurate models. This limitation is further compounded by …
data, which is crucial for training accurate models. This limitation is further compounded by …
A bidirectional multilayer contrastive adaptation network with anatomical structure preservation for unpaired cross-modality medical image segmentation
Multi-modal medical image segmentation has achieved great success through supervised
deep learning networks. However, because of domain shift and limited annotation …
deep learning networks. However, because of domain shift and limited annotation …
A 3-D anatomy-guided self-training segmentation framework for unpaired cross-modality medical image segmentation
Unsupervised domain adaptation (UDA) methods have achieved promising performance in
alleviating the domain shift between different imaging modalities. In this article, we propose …
alleviating the domain shift between different imaging modalities. In this article, we propose …
[HTML][HTML] Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
Deep neural networks are commonly used for automated medical image segmentation, but
models will frequently struggle to generalize well across different imaging modalities. This …
models will frequently struggle to generalize well across different imaging modalities. This …
FedMed-ATL: Misaligned unpaired cross-modality neuroimage synthesis via affine transform loss
The existence of completely aligned and paired multi-modal neuroimaging data has proved
its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well …
its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well …