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Found 17 papers, 2 papers with code
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.
Multi-modal vision-language model for generalizable annotation-free pathological lesions localization
Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics.
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 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.
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation
The other 100 (8, 800 image slices) are for left atrium segmentation.
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field.
Few-shot Class-incremental Learning for Cross-domain Disease Classification
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.
MGA: Medical generalist agent through text-guided knowledge transformation
To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports knowledge transformation.
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation
The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic.
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
In this work, we propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.
Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic.
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning.
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
This function adds a weighted focal coefficient and combines two traditional loss functions.
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.
