Breast mass segmentation is a prerequisite step in the use of computer‐aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuzzy boundaries of masses. In this work, we propose a mammography mass segmentation model for improving segmentation performance.

中文翻译：

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SAP‐cGAN：基于超像素平均池的数字化乳房X线照片中乳房质量分割的对抗性学习

乳房分割术是使用专为乳腺癌诊断和治疗计划设计的计算机辅助工具的必要步骤。然而，由于低对比度，不规则形状和质量边界模糊，质量分割仍然具有挑战性。在这项工作中，我们提出了一种乳腺X线摄影质量分割模型，以提高分割性能。