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Related Experiment Video

Updated: Jun 6, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Multi-scale region selection network in deep features for full-field mammogram classification.

Luhao Sun1, Bowen Han2, Wenzong Jiang3

  • 1Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China.

Medical Image Analysis
|November 30, 2024
PubMed
Summary
This summary is machine-generated.

A new deep multi-scale region selection network (MRSN) classifies mammograms without region of interest (ROI) annotations. This method effectively identifies tumors, improving early breast cancer detection and reducing diagnostic costs.

Keywords:
Breast cancerEarly diagnosisFull-field mammogramRegion selection

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis (CAD)
  • Deep Learning

Background:

  • Early breast cancer detection significantly reduces mortality, with mammography being a primary screening tool.
  • Current deep convolutional neural network (CNN) models for mammogram classification often require region of interest (ROI) or segmentation annotations, which are costly and difficult to obtain.
  • Existing methods to bypass ROI dependence increase computational load and feature redundancy.

Purpose of the Study:

  • To propose a novel deep multi-scale region selection network (MRSN) for end-to-end classification of full-field mammography images.
  • To eliminate the need for ROI or segmentation annotations in mammogram classification.
  • To improve the efficiency and accuracy of computer-aided diagnosis systems for breast cancer.

Main Methods:

  • Developed a deep multi-scale region selection network (MRSN) for feature extraction and classification.
  • MRSN filters feature information, retaining only relevant tumor region features, inspired by multi-example learning.
  • The network scores regions to identify tumor locations and selects high-scoring regions as image feature representations for focused analysis.

Main Results:

  • The proposed MRSN effectively classifies full-field mammography images without requiring ROI or segmentation data.
  • MRSN achieves performance comparable to patch-based classifiers by focusing on critical tumor regions.
  • Experimental results on public and private datasets demonstrate state-of-the-art performance for the MRSN.

Conclusions:

  • The MRSN offers a cost-effective and efficient solution for automated mammogram classification in computer-aided diagnosis.
  • This approach significantly reduces the annotation burden, facilitating wider adoption of deep learning in breast cancer screening.
  • MRSN advances the field by enabling accurate full-field mammography classification without reliance on detailed region-specific annotations.