arXiv Computer Vision
A 3D foundation model unifying CT interpretation and segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
2d ago
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
arXiv:2604.24146v2 Announce Type: replace Abstract: Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric i...
arXiv Computer Vision
Sep 10
Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks
arXiv:2505.03380v2 Announce Type: replace Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
arXiv AI
Sep 24
NV-Reason-CT: 3D Visual Language Model for CT Analysis
NV-Reason-CT is a generative vision‑language model designed for chest and abdominal CT analysis that preserves native 3D visual encoding and incorporates radiologist‑guided reasoning. The system couples a 3D vision transformer with a language model, feeding all visual tokens and their 3D coordinates directly into language decoding to maintain volumetric spatial information. Trained on a curated corpus of about 550,000 multimodal instruction examples, the model supports abnormality classification, report generation, and interactive reasoning, achieving strong performance on CT benchmarks and reducing expert interpretation time by 50%.
arXiv AI
Aug 7
Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models
arXiv:2608. 05960v1 Announce Type: cross Abstract: Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume.
arXiv Computer Vision
Sep 23
SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images
arXiv:2609.25743v1 Announce Type: new Abstract: Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and r...
arXiv AI
Aug 14
Mr3D-VL: A generalist vision language foundation model for Multiparametric 3D Magnetic Resonance Imaging
arXiv:2608. 12689v1 Announce Type: cross Abstract: Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone for brain tumor diagnosis and treatment, yet current AI models face critical limitations: their lack of natural language interaction and interpretability impedes spatial information integration and cross-modal reasoning required clinically.