MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient

• URL: http://arxiv.org/abs/2408.12236v1
• Date: Thu, 22 Aug 2024 09:10:29 GMT
• Title: MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
• Authors: Yanzeng Li, Cheng Zeng, Jinchao Zhang, Jie Zhou, Lei Zou,
• Abstract summary: This paper introduces MedDiT, a novel knowledge-controlled conversational framework that can generate plausible medical images aligned with simulated patient symptoms. MedDiT integrates various patient Knowledge Graphs (KGs), which describe the attributes and symptoms of patients, to dynamically prompt Large Language Models' (LLMs) behavior. A well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG.
• Score: 38.2964748653908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the high cost of recruiting qualified SPs and the lack of diverse medical imaging datasets have presented significant challenges. To address these issues, this paper introduces MedDiT, a novel knowledge-controlled conversational framework that can dynamically generate plausible medical images aligned with simulated patient symptoms, enabling diverse diagnostic skill training. Specifically, MedDiT integrates various patient Knowledge Graphs (KGs), which describe the attributes and symptoms of patients, to dynamically prompt Large Language Models' (LLMs) behavior and control the patient characteristics, mitigating hallucination during medical conversation. Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG. In this paper, we present the capabilities of MedDiT through a practical demonstration, showcasing its ability to act in diverse simulated patient cases and generate the corresponding medical images. This can provide an abundant and interactive learning experience for students, advancing medical education by offering an immersive simulation platform for future healthcare professionals. The work sheds light on the feasibility of incorporating advanced technologies like LLM, KG, and DiT in education applications, highlighting their potential to address the challenges faced in simulated patient-based medical education.

Related papers

• ClinKD: Cross-Modal Clinic Knowledge Distiller For Multi-Task Medical Images [4.353855760968461]
  Med-VQA (Medical Visual Question Answering) is a crucial subtask within the broader VQA (Visual Question Answering) domain. We introduce the ClinKD model, which incorporates modifications to model position encoding and a diversified training process. We achieve a new state-of-the-art performance on the Med-GRIT-270k dataset.
  arXiv  Detail & Related papers  (2025-02-09T15:08:10Z)
• Arbitrary Data as Images: Fusion of Patient Data Across Modalities and Irregular Intervals with Vision Transformers [1.194275822303467]
  Vision Transformer for irregular sampled Multi-modal Measurements (ViTiMM) Our approach, Vision Transformer for irregular sampled Multi-modal Measurements (ViTiMM), not only simplifies data preprocessing and modeling but also outperforms current state-of-the-art methods in predicting in-hospital mortality and phenotyping, as evaluated on 6,175 patients from the MIMIC-IV dataset. We hope our work inspires advancements in multi-modal medical AI by reducing the training complexity to (visual) prompt engineering, thus lowering entry barriers and enabling no-code solutions for training.
  arXiv  Detail & Related papers  (2025-01-30T09:52:15Z)
• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis [17.4235794108467]
  The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes.
  arXiv  Detail & Related papers  (2024-03-26T09:55:49Z)
• HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling [4.44283662576491]
  We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications.
  arXiv  Detail & Related papers  (2024-03-20T05:50:04Z)
• EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection [27.783012550610387]
  Cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medical imaging technologies. We propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance.
  arXiv  Detail & Related papers  (2023-11-27T05:10:15Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)

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.