32 citations · 46 across the 9 of their papers we have counts for
10 papers
Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image
Zexin Ji, Beiji Zou, Xiaoyan Kui +3
Multi-modal 3D medical image segmentation aims to accurately identify tumor regions across different modalities, facing challenges from variations in image intensity and tumor morp…
Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution
Xiaoyan Kui, Zexin Ji, Beiji Zou +5
High-resolution medical images can provide more detailed information for better diagnosis. Conventional medical image super-resolution relies on a single task which first performs…
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
Convolutional neural networks and Transformer have made significant progresses in multi-modality medical image super-resolution. However, these methods either have a fixed receptiv…
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing M…
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural netw…
Deform-Mamba Network for MRI Super-Resolution
Zexin Ji, Beiji Zou, Xiaoyan Kui +2
In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which enc…
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurat…
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Despite the increasing use of deep learning in medical image segmentation, the limited availability of annotated training data remains a major challenge due to the time-consuming d…
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentati…
Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
Haigen Hu, Leizhao Shen, Qiu Guan +3
Due to the irregular shapes,various sizes and indistinguishable boundaries between the normal and infected tissues, it is still a challenging task to accurately segment the infecte…