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Found 6 papers, 2 papers with code
HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images
While CNNs were previously augmented with attention maps or spatial masks to guide focus on potentially critical regions, learning localization guidance under heterogeneity in the spatial distribution of pathology is challenging.
Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes
To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters.
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis
Receiving brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed GraphCorr method leverages a node embedder module based on a transformer encoder to capture temporally-windowed latent representations of BOLD signals.
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss
Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories.
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss.
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines
Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch.
