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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.

Ruoyun Wang^1,2, Fanxuan Chen^1,2, Haoman Chen^1,2

¹Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China.

|June 10, 2025

Summary

Deep learning (DL) enhances digital breast tomosynthesis (DBT) for earlier breast cancer detection. While DBT offers 3D imaging, DL addresses challenges like dense breast imaging and false positives, improving screening efficiency and accuracy.

Keywords:

deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Digital breast tomosynthesis (DBT) provides high-resolution 3D mammography for enhanced breast cancer screening.
DBT presents challenges including reduced performance in dense breasts, higher false positive rates, and increased reading times.
Early breast cancer detection is critical due to rising incidence rates.

Purpose of the Study:

To review the current applications and future potential of deep learning (DL) in DBT-based breast cancer screening.
To outline the fundamentals and challenges associated with DBT technology.
To explore DL's role in improving diagnostic accuracy and processing efficiency for DBT images.

Main Methods:

Categorization of DL applications in DBT into diagnostic classification, lesion segmentation/detection, and image generation.
Summary of existing public mammography databases.
Analysis of challenges and future research directions for DL in DBT.

Main Results:

DL demonstrates effectiveness in increasing processing efficiency and diagnostic accuracy for DBT images.
Key DL applications include disease classification, lesion detection, and medical image synthesis.
Publicly available mammography datasets are summarized.

Conclusions:

DL holds significant promise for advancing DBT in breast cancer screening by overcoming current limitations.
Addressing challenges such as limited datasets and model training is crucial for DL implementation.
Future research should focus on areas like large language models, domain transfer, and data augmentation for innovative DL applications in medical imaging.