1.3k citations · 7.9k across the 178 of their papers we have counts for
11 papers · 1 filter
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Pedro R. A. S. Bassi, Wenxuan Li, Hanxue Gu +11
Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are…
NV-Reason-CT: 3D Visual Language Model for CT Analysis
Andriy Myronenko, Dong Yang, Yucheng Tang +15
We present NV-Reason-CT, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning. The model couples a n…
Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion
Paul Büschl, Ezequiel de la Rosa, Julia Wolleb +3
Implicit neural representations (INRs) can model continuous 3D shapes with a shared coordinate decoder and per-instance latent codes. At test time, autodecoder-style models commonl…
Projected Energy Matching for Generative 3D Priors
Daniel Barco, Michal Balcerak, Suprosanna Shit +4
Transport-based generative models, which learn a time-dependent vector field that moves noise to data, have become a dominant paradigm. However, these models typically do not expli…
MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors
Mohammad Golbabaee, Matteo Cencini, Carolin Pirkl +3
Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties,…
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Michal Balcerak, Suprosanna Shit, Chinmay Prabhakar +4
Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, p…
VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation
Chinmay Prabhakar, Bastian Wittmann, Tamaz Amiranashvili +6
Spatial graphs provide a lightweight and elegant representation of curvilinear anatomical structures such as blood vessels, lung airways, and neuronal networks. Accurately modeling…
SigVLP: Sigmoid Volume-Language Pre-Training for Self-Supervised CT-Volume Adaptive Representation Learning
Jiayi Wang, Hadrien Reynaud, Ibrahim Ethem Hamamci +4
Large-scale, volumetric medical imaging datasets typically aggregate scans from different vendors and devices, resulting in highly variable resolution, slice thicknesses, and numbe…
VariViT: A Vision Transformer for Variable Image Sizes
Aswathi Varma, Suprosanna Shit, Chinmay Prabhakar +5
Vision Transformers (ViTs) have emerged as the state-of-the-art architecture in representation learning, leveraging self-attention mechanisms to excel in various tasks. ViTs split…
ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation
Yuan Lin, Murong Xu, Marc Hölle +5
Widely adopted medical image segmentation methods, although efficient, are primarily deterministic and remain poorly amenable to natural language prompts. Thus, they lack the capab…
VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences
Hendrik Möller, Hanna Schoen, Robert Graf +14
The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen…