首个公开的乳腺癌异常分裂图像数据集，助力精准预测预后。
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
- 通过三位专家共识标注，构建3720张分裂图像数据集。
- 异常分裂图占比22.4%，在患者级划分下准确率达71.3%。
- 适合肿瘤病理分析与医学图像识别研究者使用。
评估组织切片中分裂核的数量是多种肿瘤（包括乳腺癌）的重要预后指标。近期研究表明，具有异常形态的分裂核（atypical mitotic figures, AMFs）可能是乳腺癌独立的预后因素。AMFs反映细胞周期调控基因突变，可导致肿瘤细胞染色体数目异常（非整倍性）。为推动该方向的模式识别研究，我们首次发布公开的正常与异常分裂核数据集（AMi-Br）。基于MIDOG 2021和TUPAC两个主流数据集，采用三位专家多数投票法对所有分裂核进行子分类。最终数据集包含3720个分裂核，涵盖223个病例，其中异常分裂核832个（22.4%），正常分裂核2888个（77.6%）。我们进行了基线分类实验，采用蒙特卡洛交叉验证和不同类别不平衡处理策略。在图像块级别划分下，平均平衡准确率最高达0.806；在患者级别划分下，最高达0.713。
原文摘要 · Abstract (English)
Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4%) and 2,888 normal MFs (77.6%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance. We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split.
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