Abstract
Atypical mitoses mark a deviation in the cell division process that can be an independent prognostically relevant marker for tumor malignancy. However, their identification remains challenging due to low prevalence, at times subtle morphological differences from normal mitoses, low inter-rater agreement among pathologists, and class imbalance in datasets. Building on the Atypical Mitosis dataset for Breast Cancer (AMi-Br), this study presents a comprehensive benchmark comparing deep learning approaches for automated atypical mitotic figure (AMF) classification, including baseline models, foundation models with linear probing, and foundation models fine-tuned with low-rank adaptation (LoRA). For rigorous evaluation, we further introduce two new hold-out AMF datasets - AtNorM-Br, a dataset of mitoses from the The TCGA breast cancer cohort, and AtNorM-MD, a multi-domain dataset of mitoses from the MIDOG++ training set. We found average balanced accuracy values of up to 0.8135, 0.7696, and 0.7705 on the in-domain AMi-Br and the out-of-domain AtNorm-Br and AtNorM-MD datasets, respectively, with the results being particularly good for LoRA-based adaptation of the Virchow-line of foundation models. Our work shows that atypical mitosis classification, while being a challenging problem, can be effectively addressed through the use of recent advances in transfer learning and model fine-tuning techniques. We make available all code and data used in this paper in this github repository: this https URL.
Abstract (translated)
非典型有丝分裂标志着细胞分裂过程中的偏差，这可能是肿瘤恶性的一个独立的预后相关标志。然而，由于其在样本中出现频率低、与正常有丝分裂形态学上的差异细微、病理学家之间的一致性较低以及数据集中的类别不平衡等问题，它们的识别仍然具有挑战性。基于乳腺癌非典型有丝分裂数据集（AMi-Br），本研究提出了一个全面的基准测试，比较用于自动分类非典型有丝分裂图（AMF）的深度学习方法，包括基线模型、通过线性探测的基础模型和采用低秩适应（LoRA）进行微调的基础模型。为了严格评估这些方法的效果，我们进一步引入了两个新的未使用数据集——AtNorM-Br，这是一个来自TCGA乳腺癌队列的有丝分裂数据集；以及AtNorM-MD，这是MIDOG++训练集中多领域有丝分裂的一个数据集。我们在内部域AMi-Br和外部域AtNorm-Br及AtNorM-MD数据集上得到了最高平均平衡精度值分别为0.8135、0.7696和0.7705，其中基于LoRA的Virchow基础模型线系适应的结果尤为出色。我们的工作表明，尽管非典型有丝分裂分类是一个具有挑战性的问题，但通过利用最近在迁移学习和模型微调技术方面的进展可以有效地解决这一问题。我们在这个GitHub仓库中提供了用于本研究的所有代码和数据：[提供链接]（请将URL替换为实际的链接地址）。
URL
https://arxiv.org/abs/2506.21444
