EchoFM: Foundation Model for Generalizable Echocardiogram Analysis

• URL: http://arxiv.org/abs/2410.23413v1
• Date: Wed, 30 Oct 2024 19:32:02 GMT
• Title: EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
• Authors: Sekeun Kim, Pengfei Jin, Sifan Song, Cheng Chen, Yiwei Li, Hui Ren, Xiang Li, Tianming Liu, Quanzheng Li,
• Abstract summary: We introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos. In EchoFM, we propose a self-supervised learning framework that captures both spatial and temporal variability. We pre-train our model on an extensive dataset comprising over 290,000 echocardiography videos, with up to 20 million frames of images.
• Score: 22.585990526913246
• License:
• Abstract: Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical foundation models have emerged, solutions for cardiac imaging, especially echocardiography videos, are still unexplored. In this paper, we introduce EchoFM, a foundation model specifically designed to represent and analyze echocardiography videos. In EchoFM, we propose a self-supervised learning framework that captures both spatial and temporal variability patterns through a spatio-temporal consistent masking strategy and periodic-driven contrastive learning. This framework can effectively capture the spatio-temporal dynamics of echocardiography and learn the representative video features without any labels. We pre-train our model on an extensive dataset comprising over 290,000 echocardiography videos covering 26 scan views across different imaging modes, with up to 20 million frames of images. The pre-trained EchoFM can then be easily adapted and fine-tuned for a variety of downstream tasks, serving as a robust backbone model. Our evaluation was systemically designed for four downstream tasks after the echocardiography examination routine. Experiment results show that EchoFM surpasses state-of-the-art methods, including specialized echocardiography methods, self-supervised pre-training models, and general-purposed pre-trained foundation models, across all downstream tasks.

Related papers

• EchoApex: A General-Purpose Vision Foundation Model for Echocardiography [9.202542805578432]
  We introduce EchoApex, the first general-purpose vision foundation model echocardiography with applications on a variety of clinical practice. Leveraging self-supervised learning, EchoApex is pretrained on over 20 million echo images from 11 clinical centres. Compared to state-of-the-art task-specific models, EchoApex attains improved performance with a unified image encoding architecture.
  arXiv  Detail & Related papers  (2024-10-14T21:10:56Z)
• EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation [1.0840985826142429]
  We introduce EchoPrime, a multi-view, view-informed, video-based vision-language foundation model trained on over 12 million video-report pairs. With retrieval-augmented interpretation, EchoPrime integrates information from all echocardiogram videos in a comprehensive study. In datasets from two independent healthcare systems, EchoPrime achieves state-of-the art performance on 23 diverse benchmarks of cardiac form and function.
  arXiv  Detail & Related papers  (2024-10-13T03:04:22Z)
• Towards a vision foundation model for comprehensive assessment of Cardiac MRI [11.838157772803282]
  We introduce a vision foundation model trained for cardiac magnetic resonance imaging (CMR) assessment. We finetune the model in supervised way for 9 clinical tasks typical to a CMR workflow. We demonstrate improved accuracy and robustness across all tasks, over a range of available labeled dataset sizes.
  arXiv  Detail & Related papers  (2024-10-02T15:32:01Z)
• Sequence-aware Pre-training for Echocardiography Probe Guidance [66.35766658717205]
  Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features. We propose a sequence-aware self-supervised pre-training method to learn personalized 2D and 3D cardiac structural features.
  arXiv  Detail & Related papers  (2024-08-27T12:55:54Z)
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training [99.2891802841936]
  We introduce the Med-ST framework for fine-grained spatial and temporal modeling. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR)
  arXiv  Detail & Related papers  (2024-05-30T03:15:09Z)
• Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets [2.0286377328378737]
  Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed.
  arXiv  Detail & Related papers  (2024-04-30T14:16:45Z)
• MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts [63.30352394004674]
  Multi-task Self-super-vised Continual Learning (MUSCLE) is a novel self-supervised pre-training pipeline for medical imaging tasks. MUSCLE aggregates X-rays collected from multiple body parts for representation learning, and adopts a well-designed continual learning procedure. We evaluate MUSCLE using 9 real-world X-ray datasets with various tasks, including pneumonia classification, skeletal abnormality classification, lung segmentation, and tuberculosis (TB) detection.
  arXiv  Detail & Related papers  (2023-10-03T12:19:19Z)
• GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis [14.737295160286939]
  Vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification. We propose a General, Echo-based, Multi-Level Transformer (GEMTrans) framework that provides explainability. We show the flexibility of our framework by considering two critical tasks including ejection fraction (EF) and aortic stenosis (AS) severity detection.
  arXiv  Detail & Related papers  (2023-08-25T07:30:18Z)
• 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)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)

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.