Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models

• URL: http://arxiv.org/abs/2404.10237v3
• Date: Sun, 1 Sep 2024 16:39:31 GMT
• Title: Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
• Authors: Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin, Li Yuan, Zuozhu Liu,
• Abstract summary: We propose a novel framework that tackles both discriminative and generative multimodal medical tasks. The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning. Our model can achieve performance superior to or on par with state-of-the-art baselines.
• Score: 17.643421997037514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making. However, they are designated for specific classification or generative tasks, and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing, hindering their clinical utility across diverse resource-constrained scenarios in practice. In this paper, we propose a novel and lightweight framework Med-MoE (Mixture-of-Experts) that tackles both discriminative and generative multimodal medical tasks. The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning. After aligning multimodal medical images with LLM tokens, we then enable the model for different multimodal medical tasks with instruction tuning, together with a trainable router tailored for expert selection across input modalities. Finally, the model is tuned by integrating the router with multiple domain-specific experts, which are selectively activated and further empowered by meta expert. Comprehensive experiments on both open- and close-end medical question answering (Med-VQA) and image classification tasks across datasets such as VQA-RAD, SLAKE and Path-VQA demonstrate that our model can achieve performance superior to or on par with state-of-the-art baselines, while only requiring approximately 30\%-50\% of activated model parameters. Extensive analysis and ablations corroborate the effectiveness and practical utility of our method.

Related papers

• MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
  We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach. MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student. We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
  arXiv  Detail & Related papers  (2025-02-03T08:50:00Z)
• Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding [9.144030136201476]
  Multimodal large language models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challenges to the development of medical MLLMs.
  arXiv  Detail & Related papers  (2024-10-31T11:07:26Z)
• FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models [54.09244105445476]
  This study introduces a novel knowledge injection approach, FedKIM, to scale the medical foundation model within a federated learning framework. FedKIM leverages lightweight local models to extract healthcare knowledge from private data and integrates this knowledge into a centralized foundation model. Our experiments across twelve tasks in seven modalities demonstrate the effectiveness of FedKIM in various settings.
  arXiv  Detail & Related papers  (2024-08-17T15:42:29Z)
• Automated Ensemble Multimodal Machine Learning for Healthcare [52.500923923797835]
  We introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies.
  arXiv  Detail & Related papers  (2024-07-25T17:46:38Z)
• UniDCP: Unifying Multiple Medical Vision-language Tasks via Dynamic Cross-modal Learnable Prompts [14.681493967465693]
  We propose UniDCP, a Unified medical vision-language model with Dynamic Cross-modal learnable Prompts. UniDCP is capable of performing all 8 medical uni-modal and cross-modal tasks over 14 corresponding datasets.
  arXiv  Detail & Related papers  (2023-12-18T13:18:24Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• Specialty-Oriented Generalist Medical AI for Chest CT Screening [14.31187762890342]
  We propose the first-of-its-kind medical multimodal-multitask foundation model (M3FM) with application in lung cancer screening and related tasks. M3FM consistently outperforms the state-of-the-art single-modal task-specific models. As a specialty-oriented generalist medical AI model, M3FM paves the way for similar breakthroughs in other areas of medicine.
  arXiv  Detail & Related papers  (2023-04-03T20:19:56Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.