MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

• URL: http://arxiv.org/abs/2501.04614v2
• Date: Thu, 09 Jan 2025 08:42:56 GMT
• Title: MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
• Authors: Daniele Molino, Francesco Di Feola, Eliodoro Faiella, Deborah Fazzini, Domiziana Santucci, Linlin Shen, Valerio Guarrasi, Paolo Soda,
• Abstract summary: We present MedCoDi-M, a model for multimodal medical data generation. We benchmark it against five competitors on the MIMIC-CXR dataset. We assess the utility of MedCoDi-M in addressing key challenges in the medical field.
• Score: 22.908801443059758
• License:
• Abstract: Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these issues, addressing data scarcity while preserving privacy. Recently, Latent Diffusion Models have emerged as a powerful tool for generating high-quality synthetic data. Meanwhile, the integration of different modalities has gained interest, emphasizing the need of models capable of handle multimodal medical data. Existing approaches struggle to integrate complementary information and lack the ability to generate modalities simultaneously. To address this challenge, we present MedCoDi-M, a 6.77-billion-parameter model, designed for multimodal medical data generation, that, following Foundation Model paradigm, exploits contrastive learning and large quantity of data to build a shared latent space which capture the relationships between different data modalities. Further, we introduce the Multi-Prompt training technique, which significantly boosts MedCoDi-M's generation under different settings. We extensively validate MedCoDi-M: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning. The results are promising, demonstrating the applicability of MedCoDi-M in medical contexts. Project page is at https://cosbidev.github.io/MedCoDi-M/.

Related papers

• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• Towards Precision Healthcare: Robust Fusion of Time Series and Image Data [8.579651833717763]
  We introduce a new method that uses two separate encoders, one for each type of data, allowing the model to understand complex patterns in both visual and time-based information. We also deal with imbalanced datasets and use an uncertainty loss function, yielding improved results. Our experiments show that our method is effective in improving multimodal deep learning for clinical applications.
  arXiv  Detail & Related papers  (2024-05-24T11:18:13Z)
• MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems [12.914295902429]
  We introduce a real world multi-modal dataset called MMIST-CCRCC. This dataset comprises 2 radiology modalities (CT and MRI), histopathology, genomics, and clinical data from 618 patients with clear cell renal cell carcinoma (ccRCC) We show that even with such severe missing rates the fusion of modalities leads to improvements in the survival forecasting.
  arXiv  Detail & Related papers  (2024-05-02T18:29:05Z)
• Capabilities of Gemini Models in Medicine [100.60391771032887]
  We introduce Med-Gemini, a family of highly capable multimodal models specialized in medicine. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them. Our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment.
  arXiv  Detail & Related papers  (2024-04-29T04:11:28Z)
• 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)
• DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency [18.291267748113142]
  We propose DrFuse to achieve effective clinical multi-modal fusion. We address the missing modality issue by disentangling the features shared across modalities and those unique within each modality. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR.
  arXiv  Detail & Related papers  (2024-03-10T12:41:34Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets [17.774341783844026]
  This work proposes Multimodal Integration of Oncology Data System (MINDS) MINDS is a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability.
  arXiv  Detail & Related papers  (2023-09-30T15:44:39Z)
• Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data [66.9359934608229]
  This study aims to initiate the development of Radiology Foundation Model, termed as RadFM. To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans. We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
  arXiv  Detail & Related papers  (2023-08-04T17:00:38Z)
• 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)
• Competence-based Multimodal Curriculum Learning for Medical Report Generation [98.10763792453925]
  We propose a Competence-based Multimodal Curriculum Learning framework ( CMCL) to alleviate the data bias and make best use of available data. Specifically, CMCL simulates the learning process of radiologists and optimize the model in a step by step manner. Experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.
  arXiv  Detail & Related papers  (2022-06-24T08:16:01Z)

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.