8 papers
CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge +1
Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, nativ…
Discrete Diffusion Language Models for Interactive Radiology Report Drafting
Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge +1
Diffusion language models, which generate text by denoising a token canvas bidirectionally instead of emitting tokens left to right, have become competitive with autoregressive (AR…
Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports
Halil Ibrahim Gulluk, Max Van Puyvelde, Wim Van Criekinge +1
In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is the change from one visit t…
BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge +1
Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories…
SDR: Set-Distance Rewards for Radiology Report Generation
Halil Ibrahim Gulluk, Max Van Puyvelde, Wim Van Criekinge +1
Reinforcement learning with verifiable rewards has rapidly advanced reasoning in vision--language models. However, for chest X-ray report generation, the standard rewards (i.e. exa…
MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification
Halil Ibrahim Gulluk, Olivier Gevaert
Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these images can vary, leading to dis…