Buchumschlag

Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosis

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Hu, Xingcan, Wang, Wei, Xiao, Li
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computer Vision and Pattern Recognition
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
von: Ward, Tyler, et al.
Veröffentlicht: (2025)

Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification
von: Yu, Guoqi, et al.
Veröffentlicht: (2026)

Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
von: Zhang, Xiaoqing, et al.
Veröffentlicht: (2023)

Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis
von: Lai, Haoran, et al.
Veröffentlicht: (2025)

Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?
von: Li, Jun, et al.
Veröffentlicht: (2026)

Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
von: Zhao, Haokai, et al.
Veröffentlicht: (2024)

Universal Semi-Supervised Learning for Medical Image Classification
von: Ju, Lie, et al.
Veröffentlicht: (2023)

Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision
von: Gao, Yunhe, et al.
Veröffentlicht: (2026)

Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentation
von: Li, Jun
Veröffentlicht: (2026)

Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
von: Yang, Yijun, et al.
Veröffentlicht: (2026)

Enhancing 3D Transformer Segmentation Model for Medical Image with Token-level Representation Learning
von: Hu, Xinrong, et al.
Veröffentlicht: (2024)

Self-Supervised Alignment Learning for Medical Image Segmentation
von: Li, Haofeng, et al.
Veröffentlicht: (2024)

Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models
von: Shi, Yuyan, et al.
Veröffentlicht: (2024)

ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis
von: Geng, Xinyu, et al.
Veröffentlicht: (2024)

Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
von: Hu, Jiesi, et al.
Veröffentlicht: (2025)

Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
von: Liu, Qianying, et al.
Veröffentlicht: (2024)

Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning
von: Chen, Liuqing, et al.
Veröffentlicht: (2025)

Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
von: Zhang, Tianwei, et al.
Veröffentlicht: (2024)

Med3D-R1: Incentivizing Clinical Reasoning in 3D Medical Vision-Language Models for Abnormality Diagnosis
von: Lai, Haoran, et al.
Veröffentlicht: (2026)

Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
von: Li, Ming, et al.
Veröffentlicht: (2024)

Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
von: Zeng, Dewen, et al.
Veröffentlicht: (2025)

Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosis
von: Nahiduzzaman, Md, et al.
Veröffentlicht: (2025)

Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models
von: Wang, Wei, et al.
Veröffentlicht: (2024)

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
von: Pan, Tan, et al.
Veröffentlicht: (2026)

Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning
von: Zheng, Fudan, et al.
Veröffentlicht: (2024)

S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation
von: Zhang, Qixiang, et al.
Veröffentlicht: (2024)

Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis
von: Ren, Sufen, et al.
Veröffentlicht: (2024)

Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images
von: Liu, Xiaoyi, et al.
Veröffentlicht: (2024)

Semi-Supervised Medical Image Segmentation via Dual Networks
von: Lu, Yunyao, et al.
Veröffentlicht: (2025)

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?
von: Zhou, Tianhong, et al.
Veröffentlicht: (2025)

Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models
von: Hu, Qingqiao, et al.
Veröffentlicht: (2024)

Autoregressive Sequence Modeling for 3D Medical Image Representation
von: Wang, Siwen, et al.
Veröffentlicht: (2024)

Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis
von: Matas, Iván, et al.
Veröffentlicht: (2025)

Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning
von: Zhou, Pengcheng, et al.
Veröffentlicht: (2025)

MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
von: Zhuang, Jiaxin, et al.
Veröffentlicht: (2024)

FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation
von: Yamamoto, Yukinori, et al.
Veröffentlicht: (2026)

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?
von: Li, Wenxuan, et al.
Veröffentlicht: (2025)

Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
von: Gao, Fei, et al.
Veröffentlicht: (2024)

Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation
von: Mishra, Suraj, et al.
Veröffentlicht: (2024)

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
von: Abhishek, Kumar, et al.
Veröffentlicht: (2026)