Article... International Conference on Learning Representations2025
Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis.
Article in ... International Conference on Learning Representations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A 3-D Cross-Modal Keypoint Descriptor for MR-US Matching and Registration.IEEE transactions on medical imaging · 2026Article
- Adaptive Riemannian optimization for multi-scale diffeomorphic matching.Nature communications · 2026Article
- Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations.Advances in neural information processing systems · 2025Article
- Article
- Deep infant brain segmentation from multi-contrast MRI.Conference record. Asilomar Conference on Signals, Systems & Computers · 2025Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a representation learning method that instead anticipates strong domain shifts at training time itself. We first propose a data engine that synthesizes highly variable training samples that would enable generalization to new biomedical contexts. To then train a single 3D network for any voxel-level task, we develop a contrastive learning method that pretrains the network to be stable against nuisance imaging variation simulated by the data engine, a key inductive bias for generalization. This network's features can be used as robust representations of input images for downstream tasks and its weights provide a strong,
Identifiers
41918964PMC13035308What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.