# Breast Lesion Diagnosis Using Static Images and Dynamic Video

arXiv: https://arxiv.org/abs/2308.09980 · HTML: https://mlsanity.com/paper/2308.09980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning](https://mlsanity.com/paper/2011.13066/) (2020)
- [HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images](https://mlsanity.com/paper/2205.08390/) (2022)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos](https://mlsanity.com/paper/2206.13318/) (2022)
- [Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention](https://mlsanity.com/paper/1903.02974/) (2019)
- [Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network](https://mlsanity.com/paper/2007.16133/) (2020)
- [An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning](https://mlsanity.com/paper/1907.00887/) (2019)

## Built on by (most cited first, 2 of 2)

- [VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos](https://mlsanity.com/paper/2507.21863/) (2025)
- [Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos](https://mlsanity.com/paper/2503.20258/) (2025)

## Abstract (arXiv)

Deep learning based Computer Aided Diagnosis (CAD) systems have been developed to treat breast ultrasound. Most of them focus on a single ultrasound imaging modality, either using representative static images or the dynamic video of a real-time scan. In fact, these two image modalities are complementary for lesion diagnosis. Dynamic videos provide detailed three-dimensional information about the lesion, while static images capture the typical sections of the lesion. In this work, we propose a multi-modality breast tumor diagnosis model to imitate the diagnosing process of radiologists, which learns the features of both static images and dynamic video and explores the potential relationship between the two modalities. Considering that static images are carefully selected by professional radiologists, we propose to aggregate dynamic video features under the guidance of domain knowledge from static images before fusing multi-modality features. Our work is validated on a breast ultrasound dataset composed of 897 sets of ultrasound images and videos. Experimental results show that our model boosts the performance of Benign/Malignant classification, achieving 90.0% in AUC and 81.7% in accuracy.