黑龙江省乳腺癌分类辅助诊断模型训练数据

通过对样本的数据处理和数据加工，提供给辅助诊断人工智能模型进行训练，帮助人工智能模型更好地理解黑龙江省样本场景下将乳腺癌分型，提取特征，发现规律，最终提高诊断人工智能模型的准确性、鲁棒性和泛化能力。1数据采集：通过正式合作协议，从医疗机构取得匿名化的样本临床数据，包括是否有术后病理结果、术后ER(雌性激素受体)、术后PR(孕激素受体)、术后Her2情况、术后Fish情况，同时还要获取系统内术后Her2阴性阳性分型标记；2数据处理：对数据进行检查核对，确保所有数据去标志化，处于完全匿名化状态且不可还原的状态，将没有病理结果的数据去除，对异常数据进行清洗去除，对部分缺失数据进行生成式补充；3数据加工：基于原始数据和算法规则，生成乳腺癌分类标记，具体判规则为：如果ER满足阳性，同时PR满足阳性，同时HER-2满足阴性，则标记为Luminal型，反之则标记为其他类型。

This dataset is prepared through sample data processing and refinement for training auxiliary diagnostic AI models, aiming to enable the models to better understand breast cancer subtyping scenarios in Heilongjiang province samples, extract features and discover underlying patterns, thereby ultimately improving the accuracy, robustness and generalization ability of the diagnostic AI models.
1. Data Collection: Through formal cooperative agreements, anonymized clinical sample data is obtained from medical institutions, including whether postoperative pathological results are available, postoperative estrogen receptor (ER) status, postoperative progesterone receptor (PR) status, postoperative human epidermal growth factor receptor 2 (HER2) status, postoperative fluorescence in situ hybridization (FISH) results, as well as postoperative HER2 negative/positive subtyping labels from the internal system.
2. Data Processing: Inspect and verify all data to ensure that all records are fully de-identified and irreversibly anonymized. Remove data without postoperative pathological results, clean and eliminate abnormal data, and perform generative supplementation for partially missing data.
3. Data Annotation and Processing: Generate breast cancer subtyping labels based on raw data and predefined algorithmic rules. The specific judgment criteria are as follows: If ER is positive, PR is positive, and HER2 is negative, the sample is labeled as Luminal subtype; otherwise, it is labeled as other subtypes.

搜集汇总

数据集介绍

特点

黑龙江省乳腺癌分类辅助诊断模型训练数据包含150条患者数据，涵盖术后病理结果、ER、PR、Her2等关键指标，用于训练乳腺癌分类辅助诊断模型，提高模型的准确性和泛化能力。