arXiv Computer Vision
Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
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Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning
arXiv:2602. 05387v2 Announce Type: replace-cross Abstract: MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation.
arXiv Computer Vision
Aug 25
DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights
arXiv:2510.14383v4 Announce Type: replace Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
arXiv AI
Jul 24
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
arXiv Machine Learning
Jun 16
MNet++: Extended 2D/3D Networks for Anisotropic Medical Image Segmentation
arXiv:2606. 15370v1 Announce Type: cross Abstract: This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation.
arXiv AI
Sep 10
EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
arXiv:2605.20470v2 Announce Type: replace-cross Abstract: Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noi...
arXiv Computer Vision
Sep 24
nnFoundation: 3D Foundation Models for Radiology
nnFoundation introduces complementary convolutional and transformer-based 3D foundation models for radiology, trained on 2.1 million CT, MRI, and PET volumes from 125 datasets. The models are evaluated on 108 tasks—including segmentation, detection, classification, report generation, and image retrieval—under domain shift, low-data, and low-compute scenarios, consistently outperforming prior 3D foundation models and training from scratch. Performance varies by task type, with convolutional models excelling at spatially localized tasks and transformer models at global semantic reasoning, and dynamic alignment with dataset characteristics further enhances transferability.