Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.

翻译：乳腺癌是全球女性健康的主要关注点，腋窝淋巴结转移的识别对预后评估和治疗指导至关重要。本文提出了一种深度学习分类流程，用于从数字核心针活检图像中量化临床信息，且比现有方法少一个步骤。使用包含1058名患者的公开数据集，评估了不同基线最先进深度学习模型基于核心针活检图像进行分类腋窝淋巴结转移状态的性能。还进行了各种数据增强技术的广泛消融研究。最后，评估了由病理学家执行的手动肿瘤分割和标注步骤。