Buchumschlag

Multimodal Medical Image Classification via Synergistic Learning Pre-training

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Lin, Qinghua, Liu, Guang-Hai, Li, Zuoyong, Li, Yang, Jiang, Yuting, Wu, Xiang
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computer Vision and Pattern Recognition Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification
von: Li, Zuoyong, et al.
Veröffentlicht: (2024)

MedFILIP: Medical Fine-grained Language-Image Pre-training
von: Liang, Xinjie, et al.
Veröffentlicht: (2025)

RegionMed-CLIP: A Region-Aware Multimodal Contrastive Learning Pre-trained Model for Medical Image Understanding
von: Fang, Tianchen, et al.
Veröffentlicht: (2025)

Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
von: Deng, Ziye, et al.
Veröffentlicht: (2025)

Vehicle-centric Perception via Multimodal Structured Pre-training
von: Wu, Wentao, et al.
Veröffentlicht: (2025)

Large-Scale 3D Medical Image Pre-training with Geometric Context Priors
von: Wu, Linshan, et al.
Veröffentlicht: (2024)

SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training
von: Zeng, Shuang, et al.
Veröffentlicht: (2025)

Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
von: Zeng, Shuang, et al.
Veröffentlicht: (2023)

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning
von: He, Yuting, et al.
Veröffentlicht: (2025)

Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation
von: Gu, Pengfei, et al.
Veröffentlicht: (2024)

Multi-modal Vision Pre-training for Medical Image Analysis
von: Rui, Shaohao, et al.
Veröffentlicht: (2024)

Bayesian Exploration of Pre-trained Models for Low-shot Image Classification
von: Miao, Yibo, et al.
Veröffentlicht: (2024)

MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning
von: Liu, Jiarun, et al.
Veröffentlicht: (2024)

LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images
von: Ding, Yujie, et al.
Veröffentlicht: (2025)

Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
von: Shen, Chuyun, et al.
Veröffentlicht: (2024)

SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
von: Shen, Lingdong, et al.
Veröffentlicht: (2024)

Addressing Imbalance for Class Incremental Learning in Medical Image Classification
von: Hao, Xuze, et al.
Veröffentlicht: (2024)

Unified Medical Image Pre-training in Language-Guided Common Semantic Space
von: He, Xiaoxuan, et al.
Veröffentlicht: (2023)

MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging
von: Kong, Shufeng, et al.
Veröffentlicht: (2025)

Zero-Training Task-Specific Model Synthesis for Few-Shot Medical Image Classification
von: Qin, Yao, et al.
Veröffentlicht: (2025)

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
von: Shao, Zibo, et al.
Veröffentlicht: (2026)

Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
von: He, Yuting, et al.
Veröffentlicht: (2026)

Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
von: Ye, Wei, et al.
Veröffentlicht: (2024)

Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
von: Xu, Yiming, et al.
Veröffentlicht: (2026)

Unleashing Diffusion and State Space Models for Medical Image Segmentation
von: Wu, Rong, et al.
Veröffentlicht: (2025)

TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data
von: Du, Siyi, et al.
Veröffentlicht: (2024)

Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
von: Dong, Wei, et al.
Veröffentlicht: (2024)

Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
von: Peng, Bincheng, et al.
Veröffentlicht: (2026)

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
von: Jiang, Chaoya, et al.
Veröffentlicht: (2023)

Revisiting Medical Image Retrieval via Knowledge Consolidation
von: Nan, Yang, et al.
Veröffentlicht: (2025)

Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
von: Chitnis, Soham Rohit, et al.
Veröffentlicht: (2023)

Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification
von: Zhu, Xun, et al.
Veröffentlicht: (2026)

Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification
von: Tsutsumi, Ayaka, et al.
Veröffentlicht: (2025)

MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging
von: Zhou, Jiaying, et al.
Veröffentlicht: (2024)

Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification
von: Oliveira, Marta, et al.
Veröffentlicht: (2023)

pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
von: Xie, Luyuan, et al.
Veröffentlicht: (2024)

Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training
von: Bawazir, Ameera, et al.
Veröffentlicht: (2024)

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
von: Liu, Che, et al.
Veröffentlicht: (2024)

A World Model of Radiologist Reading for Medical Image Representation Learning
von: Li, Yiwei, et al.
Veröffentlicht: (2026)

MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation
von: Tang, Fenghe, et al.
Veröffentlicht: (2024)