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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Related Experiment Video

Updated: Jun 24, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised

Keisuke Sugawara1, Eichi Takaya2,3, Ryusei Inamori4

  • 1Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.

Radiological Physics and Technology
|January 6, 2025
PubMed
Summary
This summary is machine-generated.

Self-supervised learning with the Jigsaw puzzle task effectively characterizes breast tissue structures for cancer classification in mammography. This approach shows potential for improving diagnostic accuracy, especially when data is limited.

Keywords:
Breast cancerBreast tissueDeep learningJigsaw puzzleMammographySelf-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Self-supervised learning (SSL) utilizes unlabeled data for deep learning in medicine.
  • The Jigsaw puzzle task in SSL learns image features and spatial relationships.
  • Current deep learning models for breast cancer diagnosis lack the comprehensive approach of human radiologists.

Purpose of the Study:

  • To evaluate the Jigsaw puzzle task's effectiveness in characterizing breast tissue for mammographic classification.
  • To compare SSL Jigsaw pre-training with other methods for breast cancer detection.

Main Methods:

  • Four pre-training pipelines were compared on the Chinese Mammography Database (CMMD): IN-Jig, Scratch-Jig, IN, and Scratch.
  • Models were fine-tuned for binary breast cancer classification.
  • Performance metrics included AUC, sensitivity, and specificity, with Grad-CAM for visualization.

Main Results:

  • The Jigsaw puzzle task-based models (IN-Jig and Scratch-Jig) achieved high AUCs (0.925 and 0.921).
  • All models demonstrated strong performance, with Jigsaw pre-training showing competitive results.
  • Detailed analysis revealed performance variations across different radiological findings and breast densities.

Conclusions:

  • The Jigsaw puzzle task is a valuable SSL pre-training method for breast cancer classification.
  • This approach can enhance diagnostic accuracy in mammography, particularly with limited labeled data.