  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.
