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Found 12 papers, 3 papers with code
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning
The first is the latent feature extractor, which primarily extracts latent space feature maps from LR dMRI and anatomical images while learning structural prior information from the anatomical images.
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.
Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement
Particularly, raw radiology reports are refined to highlight the key information according to a constructed clinical dictionary and two model-optimized knowledge-enhancement metrics.
Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications.
MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning
Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs.
Multimodal self-supervised learning for lesion localization
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking.
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities.
Online Joint Assortment-Inventory Optimization under MNL Choices
We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori.
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability.
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data.
Structural Residual Learning for Single Image Rain Removal
Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages.
Robust Mean Field Social Optimal Control with Applications to Opinion Dynamics
By the variational method, the social optimal problem is analyzed, and the equivalent robust optimal control problems are obtained for each agent.
Optimization and Control
