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Related Experiment Video

Updated: Feb 14, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Robust Dual-Stream Diagnosis Network for Ultrasound Breast Tumor Classification with Cross-Domain Segmentation

Xiaokai Jiang¹, Xuewen Ding¹, Jinying Ma¹

¹School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.

Sensors (Basel, Switzerland)

|February 13, 2026

Summary

A new Dual-Stream Diagnosis Network (DSDNet) improves breast cancer diagnosis from ultrasound images. This method enhances accuracy by using segmentation priors to guide classification, overcoming challenges like noise and variable image quality for better early detection.

Keywords:

breast cancer diagnosis feature fusion segmentation prior guidance transformer block ultrasound image analysis

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Ultrasound imaging is crucial for early breast cancer detection, but image interpretation is difficult due to noise, low contrast, and anatomical complexity.
Variability in ultrasound images across different settings hinders the development of reliable computer-aided diagnostic systems.

Purpose of the Study:

To develop a robust breast tumor diagnosis strategy for ultrasound imaging that overcomes current interpretation challenges.
To introduce a novel Dual-Stream Diagnosis Network (DSDNet) for accurate and stable breast cancer classification.

Main Methods:

A cross-domain segmentation prior guided classification strategy using a Dual-Stream Diagnosis Network (DSDNet).
DSDNet features a frozen segmentation branch providing spatial priors to a classification backbone.
Novel modules include Dual-Stream Mask Attention (DSMA), Segmentation Prior Guidance Fusion (SPGF), and Mamba-Inspired Linear Transformer (MILT) blocks.

Main Results:

DSDNet achieved high accuracy (ACC) on benchmark datasets: 0.878 (BUSI), 0.836 (BUS), and 0.882 (GDPH_SYSUCC).
Excellent Recall scores were obtained: 0.866 (BUSI), 0.789 (BUS), and 0.878 (GDPH_SYSUCC).
The proposed method demonstrated stable and effective performance across diverse imaging conditions.

Conclusions:

The DSDNet framework provides a robust and effective solution for breast tumor diagnosis in ultrasound imaging.
The cross-domain segmentation prior guidance strategy significantly enhances classification performance and generalizability.