4 citations · 12 across the 12 of their papers we have counts for
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Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning
Zecheng Liu, Jia Wei, Rui Li
Due to the difficulties of obtaining multimodal paired images in clinical practice, recent studies propose to train brain tumor segmentation models with unpaired images and capture…
SFusion: Self-attention based N-to-One Multimodal Fusion Block
Zecheng Liu, Jia Wei, Rui Li +1
People perceive the world with different senses, such as sight, hearing, smell, and touch. Processing and fusing information from multiple modalities enables Artificial Intelligenc…
Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation
Zekang Chen, Jia Wei, Rui Li
In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided…
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation
Zhaotao Wu, Jia Wei, Jiabing Wang +1
We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and the…
Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation
Shuai Wang, Rui Li
Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting.However, previous methods onl…