Keywords: Segmentation, Breast
Motivation: Automating and optimising the segmentation process for breast tumours aims to reduce the workload of radiologists and improve the consistency and reliability of segmentation.
Goal(s): To develop a 3D DCE-MRI-based deep network architecture that would achieve fully automated segmentation of breast cancer lesions.
Approach: A dataset consists of 622 (298+128+196) anonymized DCE–MRI scans was collected. We proposed the attention and transformer based method for 3D breast tumor segmentation, incorporating Residual and UNet modules to enhance feature relevance and capture multi-scale context.
Results: Our models have achieved significant results in automated breast cancer lesion identification and segmentation.
Impact: By constructing and training an efficient deep learning model to achieve high-precision segmentation of breast cancer lesions, it provides a powerful auxiliary tool for clinical diagnosis, treatment planning and prognosis analysis.
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