511 citations · 1.3k across the 32 of their papers we have counts for
12 papers · 1 filter
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…
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…
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Marija Pizurica, Eric Zimmermann, Neil Tenenholtz +5
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingl…
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…
OpenMedQ: Broad Open Pretraining for Medical Vision-Language Models
Ibrahim Gulluk, Max Van Puyvelde, Olivier Gevaert
We present OpenMedQ, a medical vision-language model pretrained on the broadest fully-open medical mix to date: 14 datasets totaling ~3.35M pretraining samples spanning pathology,…
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…
SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model
Xianghao Zhan, Jingyu Xu, Yuanning Zheng +2
Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM…