# A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

arXiv: https://arxiv.org/abs/2207.00141 · HTML: https://mlsanity.com/paper/2207.00141/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [Deep Feature Flow for Video Recognition](https://mlsanity.com/paper/1611.07715/) (2016)
- [Flow-Guided Feature Aggregation for Video Object Detection](https://mlsanity.com/paper/1703.10025/) (2017)
- [Memory Enhanced Global-Local Aggregation for Video Object Detection](https://mlsanity.com/paper/2003.12063/) (2020)
- [Sequence Level Semantics Aggregation for Video Object Detection](https://mlsanity.com/paper/1907.06390/) (2019)
- [Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution](https://mlsanity.com/paper/1909.06720/) (2019)
- [Temporal RoI Align for Video Object Recognition](https://mlsanity.com/paper/2109.03495/) (2021)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
- [Semi-supervised Breast Lesion Detection in Ultrasound Video Based on Temporal Coherence](https://mlsanity.com/paper/1907.06941/) (2019)

## Built on by (most cited first, 30 of 32)

- [Vivim: a Video Vision Mamba for Medical Video Segmentation](https://mlsanity.com/paper/2401.14168/) (2024)
- [Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos](https://mlsanity.com/paper/2310.01861/) (2023)
- [From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning](https://mlsanity.com/paper/2411.16380/) (2024)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding](https://mlsanity.com/paper/2505.17779/) (2025)
- [Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers](https://mlsanity.com/paper/2306.06877/) (2023)
- [A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos](https://mlsanity.com/paper/2309.04702/) (2023)
- [Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound](https://mlsanity.com/paper/2407.00267/) (2024)
- [Mixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark](https://mlsanity.com/paper/2410.12143/) (2024)
- [MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis](https://mlsanity.com/paper/2307.07807/) (2023)
- [Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses](https://mlsanity.com/paper/2407.16634/) (2024)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos](https://mlsanity.com/paper/2507.21863/) (2025)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound](https://mlsanity.com/paper/2602.19322/) (2026)
- [Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection](https://mlsanity.com/paper/2305.18060/) (2023)
- [Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning](https://mlsanity.com/paper/2306.03835/) (2023)
- [Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection](https://mlsanity.com/paper/2408.14051/) (2024)
- [SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance](https://mlsanity.com/paper/2504.00161/) (2025)
- [COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets](https://mlsanity.com/paper/2508.09886/) (2025)
- [Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs](https://mlsanity.com/paper/2511.07748/) (2025)
- [Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation](https://mlsanity.com/paper/2411.02441/) (2024)
- [Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis](https://mlsanity.com/paper/2503.17475/) (2025)
- [Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos](https://mlsanity.com/paper/2503.20258/) (2025)
- [Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection](https://mlsanity.com/paper/2504.11779/) (2025)
- [TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis](https://mlsanity.com/paper/2507.18288/) (2025)
- [Deep Attention-guided Adaptive Subsampling](https://mlsanity.com/paper/2510.12376/) (2025)
- [Prior-Guided DETR for Ultrasound Nodule Detection](https://mlsanity.com/paper/2601.02212/) (2026)
- [Prompt-Free SAM-Based Multi-Task Framework for Breast Ultrasound Lesion Segmentation and Classification](https://mlsanity.com/paper/2601.05498/) (2026)
- [Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals](https://mlsanity.com/paper/2602.23899/) (2026)

## Abstract (arXiv)

Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at urlhttps://github.com/jhl-Det/CVA-Net.