arXiv Computer Vision
Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models
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
6d ago
Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
arXiv:2608.24281v1 Announce Type: new Abstract: Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object de...
arXiv AI
Jun 19
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.
arXiv AI
Jul 28
Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration
arXiv:2607. 23343v1 Announce Type: cross Abstract: Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions.
arXiv AI
Jul 7
CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training
arXiv:2607. 02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv AI
Jul 8
CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv Machine Learning
Jun 2
PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
arXiv:2606. 01537v1 Announce Type: cross Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data.