mlsanityData to 14 Oct 2026

Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers

arXiv 2306.06877 ↗First posted 12 Jun 2023cs.CVcited by 5

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Abstract (arXiv)

Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection of ultrasound video datasets is much harder. In this paper, we explore the feasibility of enhancing the performance of ultrasound video classification using the static image dataset. To this end, we propose KGA-Net and coherence loss. The KGA-Net adopts both video clips and static images to train the network. The coherence loss uses the feature centers generated by the static images to guide the frame attention in the video model. Our KGA-Net boosts the performance on the public BUSV dataset by a large margin. The visualization results of frame attention prove the explainability of our method. The codes and model weights of our method will be made publicly available.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

16 of 29 references.

References · 16

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  12. 2018BUSIS: A Benchmark for Breast Ultrasound Image Segmentationcited by 32
4 more
  1. 2022A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videoscited by 32
  2. 2021Vision Transformer for Classification of Breast Ultrasound Imagescited by 21
  3. 2022Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videoscited by 8
  4. 2022Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasoundcited by 3

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Modelscited by 12
  2. 2025Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasoundcited by 2
  3. 2025Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMscited by 1
  4. 2025Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videoscited by 0
  5. 2025A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scanscited by 0