Machine Learning-Based Automating Breast Cancer Detection and Classification using DWI
Motivation: Breast MRI's subotimal specificity and labor-intensive DWI interpretation call for automated diagnostic solutions. Goal(s): Investigate effectiveness of AI integration for breast tumor detection and characterization in DWI. Approach: Retrospective analysis of 601 patients. Fine-tuned YOLO v5 for tumor identification and 2D CNN for malignancy assessment, utilizing whole-slice and bounding box techniques. Results: Strong diagnostic performance observed. Whole-slice method: AUC 0.90, sensitivity 84.4%, specificity 84.0%. Bounding box method: AUC 0.87, sensitivity 87.5%, specificity 80.0%. Impact: Potential to boost screening efficiency, minimize false positives, and improve patient care via more precise, swift diagnoses.
Authors
- Keiho Imanishi
- Shinji Naganawa
- Yunhao Zhang (ORCID: https://orcid.org/0009-0009-3182-9296)
- Akihiko Minami (ORCID: https://orcid.org/0009-0004-0882-0019)
- Rintaro Ito (ORCID: https://orcid.org/0000-0002-7971-043X)
- Ryosuke Mizuno (ORCID: https://orcid.org/0009-0008-3351-8910)
- Masako Kataoka (ORCID: https://orcid.org/0000-0002-6212-3351)
- Maya Honda (ORCID: https://orcid.org/0000-0002-5337-3455)
- Mami Iima (ORCID: https://orcid.org/0000-0002-1991-2494)
- Hiroko Satake (ORCID: https://orcid.org/0000-0001-5208-2018)
- Yuji Nakamoto
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/0111
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00