1.3k citations · 7.9k across the 178 of their papers we have counts for
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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…
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…
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
Junqi Liu, Zejun Wu, Pedro R. A. S. Bassi +15
Image enhancement improves visual quality and helps reveal details that are hard to see in the original image. In medical imaging, it can support clinical decision-making, but curr…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT
Matan Atad, Alexander W. Marka, Lisa Steinhelfer +10
Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often r…
Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging
Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit +7
Recent progress in vision-language modeling for 3D medical imaging has been fueled by large-scale computed tomography (CT) corpora with paired free-text reports, stronger architect…
RadDiagSeg-M: A Vision Language Model for Joint Diagnosis and Multi-Target Segmentation in Radiology
Chengrun Li, Corentin Royer, Haozhe Luo +6
Most current medical vision language models struggle to jointly generate diagnostic text and pixel-level segmentation masks in response to complex visual questions. This represents…
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
Pedro R. A. S. Bassi, Xinze Zhou, Wenxuan Li +20
Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screenin…
Circle of Willis Centerline Graphs: A Dataset and Baseline Algorithm
Fabio Musio, Norman Juchler, Kaiyuan Yang +4
The Circle of Willis (CoW) is a critical network of arteries in the brain, often implicated in cerebrovascular pathologies. Voxel-level segmentation is an important first step towa…
MInDI-3D: Iterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography
Daniel Barco, Marc Stadelmann, Martin Oswald +7
We present MInDI-3D (Medical Inversion by Direct Iteration in 3D), the first 3D conditional diffusion-based model for real-world sparse-view Cone Beam Computed Tomography (CBCT) ar…
BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +26
BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provi…
Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling
Chinmay Prabhakar, Suprosanna Shit, Tamaz Amiranashvili +2
3D spatial graphs play a crucial role in biological and clinical research by modeling anatomical networks such as blood vessels,neurons, and airways. However, generating 3D biologi…
Parametric shape models for vessels learned from segmentations via differentiable voxelization
Alina F. Dima, Suprosanna Shit, Huaqi Qiu +8
Vessels are complex structures in the body that have been studied extensively in multiple representations. While voxelization is the most common of them, meshes and parametric mode…
Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
Hendrik Möller, Hanna Schön, Alina Dima +10
Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies an…
PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI
Hendrik Möller, Lukas Krautschick, Matan Atad +9
Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often di…
Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models
Zeineb Haouari, Jonas Weidner, Yeray Martin-Ruisanchez +5
Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equation-based models offer promising…
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning
Francesco Girlanda, Olga Demler, Bjoern Menze +1
Accurate prediction of cardiovascular diseases remains imperative for early diagnosis and intervention, necessitating robust and precise predictive models. Recently, there has been…
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning
Yuqing Huang, Bastian Wittmann, Olga Demler +2
Early identification of stroke is crucial for intervention, requiring reliable models. We proposed an efficient retinal image representation together with clinical information to c…