Sep 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 96 references
TL;DR
A novel three-axis taxonomy covering Gating Architecture, Application Level, and Supporting Techniques is proposed and a controlled comparison of six gating architectures on three biomedical benchmarks shows that the five Axis 1 gating types produce measurably distinct performance profiles and that no single architecture dominates across all settings.
This work presents a hierarchical multimodal MoE for interstitial lung disease (ILD) classification that integrates a frozen, pre-trained imaging expert with structured electronic health records (EHR) via two-stage gating via two-stage gating.
A. Peltekian, Gorkem Durak, H. Aktas et al.· 0 citations
Recent advances in large language models (LLMs) and vision transformers have enabled multimodal systems that integrate clinical text with medical imaging for diagnostic decision-making. While these systems show promising results on benchmark datasets in well-resourced research settings, their applicability in low-resou...
Kahakashan Ashraf, Md.Hamid Hosen, N. Farah et al.· Frontiers in Digital Health· 0 citations
Inspired by diagnostic practice synergizing numerical assessment, visual inspection and clinical context, MedTVL is introduced, a text-guided dual-pathway architecture tailored for MedTS classification that synergizes a convolution-based temporal pathway for fine-grained temporal dynamics from raw numerical sequences a...
Jiexia Ye, Jia Li, F. Tsung· Proceedings of the 32nd ACM...· 0 citations
A new confidence-aware hybrid design, CARE-LLM-GRAPH, which combines large language models (LLMs) to perform clinical reasoning, multimodal deep learning to analyze medical images, and population-aware graph intelligence to provide cohort-level information is presented.
Unknown authors· European Journal of Prosthod...· 0 citations
Traditional diagnostic methods in biomedicine are often constrained by subjectivity, misdiagnosis, and the inability to efficiently process large, complex, and multimodal datasets. Recent advances in deep learning and hybrid architectures have enabled the automated learning of discriminative representations from high‐d...
Li-Feng Li, Ashfaque Khowaja, Yu-Cheng Song et al.· Medicine Advances· 0 citations
Generalist-Specialist-MoE (GS-MoE), a two-branch architecture that couples a cross-modal generalist model with distinct modality-specific specialists (experts) via domain-constrained feature fusion, is introduced.
Johannes Kaiser, Florian Braunmiller, D. Rückert et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
