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Found 10 papers, 5 papers with code
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities.
Sample-Specific Debiasing for Better Image-Text Models
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval.
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
Using Multiple Instance Learning to Build Multimodal Representations
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e. g., image classification, visual grounding, and cross-modal retrieval.
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography
We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography.
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks.
Peak learning of mass spectrometry imaging data using artificial neural networks
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis.
Equivariant Filters for Efficient Tracking in 3D Imaging
The transformation is then derived in closed form from the outputs of the filters.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
DEMI: Discriminative Estimator of Mutual Information
Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.
