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Found 12 papers, 3 papers with code
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.
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.
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.
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.
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models
Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages.
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.
Meta-DM: Applications of Diffusion Models on Few-Shot Learning
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies.
Few-Shot Learning
Unsupervised Few-Shot Image Classification
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.
Agreement or Disagreement in Noise-tolerant Mutual Learning?
The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network.
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
With the development of deep learning, medical image classification has been significantly improved.
