4 citations · 4 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Improving Medical VQA through Trajectory-Aware Process Supervision
Halil Ibrahim Gulluk, Olivier Gevaert
Reasoning capabilities are crucial for reliable medical visual question answering (VQA); however, existing datasets rarely include reasoning explanations. We address this by genera…
SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning
Halil Ibrahim Gulluk, Olivier Gevaert
Medical vision-language datasets are often limited in size and biased toward negative findings, as clinicians report abnormalities mostly but might omit some positive/neutral findi…
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng +3
Language-supervised vision models have recently attracted great attention in computer vision. A common approach to build such models is to use contrastive learning on paired data a…
Globally Optimal Training of Neural Networks with Threshold Activation Functions
Tolga Ergen, Halil Ibrahim Gulluk, Jonathan Lacotte +1
Threshold activation functions are highly preferable in neural networks due to their efficiency in hardware implementations. Moreover, their mode of operation is more interpretable…
Towards Sample-efficient Overparameterized Meta-learning
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk +2
An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain th…
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak +1
Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from comm…