AIMC Topic: Multimodal Imaging

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IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

NeuroImage
Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its ear...

Enhancing Diagnosis of Psoriasis and Inflammatory Skin Diseases: A Spatially Aligned Multimodal Model Integrating Clinical and Dermoscopic Images.

The Journal of investigative dermatology
Psoriasis is a chronic inflammatory disease with significant physical and psychological impacts. To overcome the limitations of single-modality artificial intelligence models in diagnosing inflammatory skin diseases, we propose a multimodal framework...

Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.

Medical image analysis
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...

MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...

MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.

Cell reports. Medicine
Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practiti...

Multimodal imaging platform for enhanced tumor resection in neurosurgery: integrating hyperspectral and pCLE technologies.

International journal of computer assisted radiology and surgery
PURPOSE: This work presents a novel multimodal imaging platform that integrates hyperspectral imaging (HSI) and probe-based confocal laser endomicroscopy (pCLE) for improved brain tumor identification during neurosurgery. By combining these two modal...

Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation.

IEEE transactions on medical imaging
Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contribution...

Multimodal medical image fusion combining saliency perception and generative adversarial network.

Scientific reports
Multimodal medical image fusion is crucial for enhancing diagnostic accuracy by integrating complementary information from different imaging modalities. Current fusion techniques face challenges in effectively combining heterogeneous features while p...

Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma.

EBioMedicine
BACKGROUND: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and neck cancer characterized with improved clinical outcome and better response to therapy. Pathology an...

Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, which is crucial for clinical diagnosis and treatment planning. However, a major challenge in utilizing ...