Spatio-Temporal Fusion Model for Standard View Classification of Echocardiographic Videos

• URL: http://arxiv.org/abs/2606.17437v1
• Date: Tue, 16 Jun 2026 02:37:43 GMT
• Title: Spatio-Temporal Fusion Model for Standard View Classification of Echocardiographic Videos
• Abstract summary: We release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views.<n>We benchmark representative video classification architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (NNs), and Transformers.<n>We propose a Spatio-Temporal Fusion Model (STFM) framework that jointly captures spatial anatomical structures and temporal cardiac dynamics.
• Score: 22.11102645825999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges. First, publicly available datasets are scarce and limited in scale and view coverage. Second, the performance of some modern video-level architectures for echocardiographic view classification remains underexplored. Third, some view categories exhibit highly similar spatial appearances, making single-frame features insufficient for discrimination, while heterogeneous frame quality complicates robust temporal information fusion. To address these challenges, we release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views, which is, to the best of our knowledge, the largest publicly available echocardiography video dataset. Using EV9V, we systematically benchmark representative video classification architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Furthermore, we propose a Spatio-Temporal Fusion Model (STFM), an efficient dual-stream CNN-LSTM (Long Short-Term Memory) framework that jointly captures spatial anatomical structures and temporal cardiac dynamics. The proposed framework leverages uncertainty-aware learning to preferentially sample representative video segments during training and evidence-based fusion during inference, improving robustness to variations in frame quality across echocardiographic videos. Extensive experiments demonstrate that our method achieves competitive performance across diverse video classification models, validating the effectiveness of uncertainty-aware spatio-temporal learning for echocardiographic view classification. The code is available at https://github.com/bgx666/stfm.

Related papers

• Cohort-Scale Neural Atlases of Ultrasound Video [2.4814632441414552]
  We introduce a cohort-scale neural atlas for ultrasound video analysis.<n>Our method learns coherent canonical templates and enables accurate atlas-space annotation transfer.<n>On EchoNet-Dynamic and MSK-Bone, it supports single- and few-shot transfer with accuracy competitive with strong dense-correspondence baselines.
  arXiv  Detail & Related papers  (2026-05-30T20:52:02Z)
• Learning Spatio-Temporal Feature Representations for Video-Based Gaze Estimation [50.05866669110754]
  Video-based gaze estimation methods aim to capture the inherently temporal dynamics of human eye gaze from multiple image frames.<n>We propose the Spatio-Temporal Gaze Network (ST-Gaze), a model that combines a CNN backbone with dedicated channel attention and self-attention modules.<n>We show that ST-Gaze achieves state-of-the-art performance both with and without person-specific adaptation.
  arXiv  Detail & Related papers  (2025-12-19T15:15:58Z)
• Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation [21.738308923180767]
  We present DISCOVR, a self-supervised dual branch framework for cardiac ultrasound video representation learning.<n>DISCOVR combines a clustering-based video encoder that models temporal dynamics with an online image encoder that extracts fine-grained spatial semantics.
  arXiv  Detail & Related papers  (2025-06-13T13:36:33Z)
• StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning [79.44594332189018]
  Class-Incremental Learning (CIL) seeks to develop models that continuously learn new action categories over time without previously acquired knowledge.<n>Existing approaches either rely on forgetting, raising concerns over memory and privacy, or adapt static image-based methods that neglect temporal modeling.<n>We propose a unified and exemplar-free VCIL framework that explicitly disentangles and preserves information.
  arXiv  Detail & Related papers  (2025-05-20T06:46:51Z)
• Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation [10.789826145990016]
  This paper presents a deep learning framework for medical video segmentation. Our framework explicitly extracts features from neighbouring frames across the temporal dimension. It incorporates them with a temporal feature blender, which then tokenises the high-level-temporal feature to form a strong global feature encoded via a Swin Transformer.
  arXiv  Detail & Related papers  (2023-02-22T12:09:39Z)
• Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation [11.575821326313607]
  We propose Video-TransUNet, a deep architecture for segmentation in medical CT videos constructed by integrating temporal feature blending into the TransUNet deep learning framework. In particular, our approach amalgamates strong frame representation via a ResNet CNN backbone, multi-frame feature blending via a Temporal Context Module, and reconstructive capabilities for multiple targets via a UNet-based convolutional-deconal architecture with multiple heads.
  arXiv  Detail & Related papers  (2022-08-17T14:28:58Z)
• Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems [0.0]
  Long-term motion patterns alone play a pivotal role in the task of recognizing an event. We show that the long-term motion patterns alone play a pivotal role in the task of recognizing an event. Only the temporal features are exploited using a hybrid Convolutional Neural Network (CNN) + Recurrent Neural Network (RNN) architecture.
  arXiv  Detail & Related papers  (2021-11-03T08:30:38Z)
• Efficient Modelling Across Time of Human Actions and Interactions [92.39082696657874]
  We argue that current fixed-sized-temporal kernels in 3 convolutional neural networks (CNNDs) can be improved to better deal with temporal variations in the input. We study how we can better handle between classes of actions, by enhancing their feature differences over different layers of the architecture. The proposed approaches are evaluated on several benchmark action recognition datasets and show competitive results.
  arXiv  Detail & Related papers  (2021-10-05T15:39:11Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Is Space-Time Attention All You Need for Video Understanding? [50.78676438502343]
  We present a convolution-free approach to built exclusively on self-attention over space and time. "TimeSformer" adapts the standard Transformer architecture to video by enabling feature learning from a sequence of frame-level patches. TimeSformer achieves state-of-the-art results on several major action recognition benchmarks.
  arXiv  Detail & Related papers  (2021-02-09T19:49:33Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)

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.