mlsanityData to 14 Oct 2026

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

arXiv 2304.06662 ↗First posted 13 Apr 2023eess.IV cs.CVcited by 11

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Abstract (arXiv)

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 145 of its 642 references that are in the map. First posted on arXiv 13 Apr 2023.

Builds on

145 of 642 references.

References · 145

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50 most cited of 145 All 145 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 0; 2026: 3.

11 papers.

  1. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  2. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teachercited by 9
  3. 2024Longitudinal Mammogram Risk Predictioncited by 8
  4. 2024A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRIcited by 4
  5. 2023Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancercited by 2
  6. 2024Enhancing Osteoporosis Detection: An Explainable Multi-Modal Learning Framework with Feature Fusion and Variable Clusteringcited by 2
  7. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  8. 2024Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainabilitycited by 0
  9. 2026Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosiscited by 0
  10. 2026Analyzing Image Encoder Choices and Graph Homophily in GCN Frameworks for Breast Ultrasound Classificationcited by 0
  11. 2026A Concept-informed Interpretable Artificial Intelligence Framework for Breast Ultrasound Diagnosiscited by 0