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Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA

Jiale Jiang1, Junchuan Peng1, Chuting Hu2

  • 1School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, Guangdong, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen 518060, Guangdong, China.

Artificial Intelligence in Medicine
|December 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for automatic breast cancer detection and classification. The novel approach enhances diagnostic efficiency by accurately identifying and categorizing breast lesions from mammograms.

Keywords:
Breast cancerBreast lesion detectionDeep learningObject detection algorithmWhole mammogram classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Deep learning models are increasingly utilized for medical image analysis.
  • Accurate breast cancer detection and classification are crucial for effective treatment planning.
  • Existing methods face challenges in detecting subtle lesions and reducing false positives.

Purpose of the Study:

  • To develop a robust deep learning framework for automated breast lesion detection and classification.
  • To improve the accuracy and efficiency of mammogram interpretation for radiologists.
  • To enhance the classification of mammograms as benign or malignant.

Main Methods:

  • Proposed a three-stage deep learning framework utilizing Probabilistic Anchor Assignment (PAA), an anchor-free object detection algorithm.
  • Implemented a two-branch ROI detector with threshold-adaptive post-processing for lesion classification and false positive reduction.
  • Integrated an ROI classifier and an image classifier using both local and global features for improved classification performance.

Main Results:

  • The framework successfully detected and classified breast lesions (masses and calcifications) with high accuracy.
  • The proposed method demonstrated improved performance in classifying mammograms as benign or malignant.
  • Evaluated on three public datasets (CBIS-DDSM, INbreast, MIAS), outperforming state-of-the-art methods.

Conclusions:

  • The developed deep learning framework significantly enhances diagnostic efficiency in breast cancer screening.
  • The automated detection and classification of breast lesions aid radiologists in making more accurate diagnoses.
  • This approach holds promise for improving breast cancer diagnosis and patient outcomes.