mlsanityData to 7 Oct 2026

Enhancing Medical Cross-Modal Hashing Retrieval using Dropout-Voting Mixture-of-Experts Fusion

arXiv 2512.06449 ↗First posted 6 Dec 2025cs.IRcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

In recent years, cross-modal retrieval using images and text has become an active area of research, especially in the medical domain. The abundance of data in various modalities in this field has led to a growing importance of cross-modal retrieval for efficient image interpretation, data-driven diagnostic support, and medical education. In the context of the increasing integration of distributed medical data across healthcare facilities with the objective of enhancing interoperability, it is imperative to optimize the performance of retrieval systems in terms of the speed, memory efficiency, and accuracy of the retrieved data. This necessity arises in response to the substantial surge in data volume that characterizes contemporary medical practices. In this study, we propose a novel framework that incorporates dropout voting and mixture-of-experts (MoE) based contrastive fusion modules into a CLIP-based cross-modal hashing retrieval structure. We also propose the application of hybrid loss. So we now call our model MCMFH which is a medical cross-modal fusion hashing retrieval. Our method enables the simultaneous achievement of high accuracy and fast retrieval speed in low-memory environments. The model is demonstrated through experiments on radiological and non-radiological medical datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 11 of its 31 references that are in the map. First posted on arXiv 6 Dec 2025.

Builds on

11 of 31 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  5. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  6. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  7. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
  8. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 506
  9. 2022A Review of Sparse Expert Models in Deep Learningcited by 147
  10. 2016A Survey on Learning to Hashcited by 119
  11. 2016Deep Cross-Modal Hashingcited by 54

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026HMAR: Hierarchical Modality-Aware Expert and Dynamic Routing Medical Image Retrieval Architecture
  2. 2025PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval
  3. 2024Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
  4. 2026Multi-task Cross-modal Learning for Chest X-ray Image Retrieval
  5. 2024Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach
  6. 2023Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval
  7. 2025M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
  8. 2025ICA-RAG: Information Completeness Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis