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Found 14 papers, 9 papers with code
ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction
To address these challenges, we propose an innovative multimodal approach for predicting MCI conversion, focusing specifically on the issues of missing positron emission tomography (PET) data and integrating diverse medical information.
VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection
The early detection of glottic carcinoma is critical for improving patient outcomes, as it enables timely intervention, preserves vocal function, and significantly reduces the risk of tumor progression and metastasis.
Stain-aware Domain Alignment for Imbalance Blood Cell Classification
In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification.
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation
The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment.
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region.
A Survey of Stance Detection on Social Media: New Directions and Perspectives
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes.
Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversations
MaTAV is with the advantages of aligning unimodal features to ensure consistency across different modalities and handling long input sequences to better capture contextual multimodal information.
3D-LSPTM: An Automatic Framework with 3D-Large-Scale Pretrained Model for Laryngeal Cancer Detection Using Laryngoscopic Videos
Laryngeal cancer is a malignant disease with a high morality rate in otorhinolaryngology, posing an significant threat to human health.
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification.
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions.
SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection
However, they are still limited in their capabilities to accurately locate the lesion region and capture the discriminative feature information between the global and local branches.
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning
\method{} comprises several key modules, including the core subgraph knowledge submodule, graph domain adaptation module, and few-shot learning module for downstream tasks.
UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts
Emotional Text-to-Speech (E-TTS) synthesis has garnered significant attention in recent years due to its potential to revolutionize human-computer interaction.
Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework
Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection.
