662-03-004 ISMRM Abstract

3D MRI-Based Breast Cancer Classification: Leveraging Segmentation-Guided Multi-Modality Fusion

Primary: Body - Breast

Secondary: Analysis Methods - Classification and Prediction

662-03-004 · Classification: Other Cancer · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row C

Keywords: Breast MRI Automated segmentation 3D CNNs Classification

Accepted

Kaiting Wang^1,2, Pengchen Liang ³, Zhifeng Chen⁴, Xiaoyun Liang³, Darong Zhu⁵, Shiwei Wang^1,2

¹The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China

²The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China

³Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Shanghai, China

⁴Neusoft Medical Systems Co., Ltd., Hangzhou, China

⁵Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China

Presenting Author: Pengchen Liang

Synopsis

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References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. doi:10.3322/caac.21834 [doi]

2. Ronneberger O, et al. U‑Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI, 2015.

3. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z [doi]

4. Jones LI, Marshall A, Geach R, et al. Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset. Breast Cancer Res. 2024;26(1):85. Published 2024 May 28. doi:10.1186/s13058-024-01846-1 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/662-03-004