山东省HER2型乳腺癌辅助诊断模型训练数据

数据应用: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山东省样本场景下HER2型乳腺癌的情况，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Her2情况，术后fish情况；2数据处理：对数据进行检查核对，确保所有数据去标识化，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：基于原始数据以及算法规则HER2型乳腺癌的术后状态，生成阴性阳性分型标记，具体规则为：如果Her2满足3+或Her2满足2+且同时术后Fish为扩增则标记为阳性，其余标记为阴性。Data Application: This dataset is processed and curated from patient samples, and provided for training auxiliary diagnostic AI models, to help the models better comprehend the characteristics of HER2 subtype breast cancer in the context of patient samples from Shandong Province, extract features, discover underlying patterns, and ultimately improve the accuracy, robustness and generalization ability of the diagnostic AI models. 1 Data Collection: Obtain anonymized clinical data of patient samples from medical institutions through formal cooperation agreements, including the availability of postoperative pathological results, postoperative Her2 status, and postoperative FISH test results. 2 Data Processing: Conduct inspection and validation on the dataset, ensure that all data is de-identified, fully anonymized and irreversibly non-reidentifiable, remove entries without postoperative pathological results, clean and filter out abnormal data, and perform generative supplementation for partially missing data entries. 3 Data Curation: Based on the original dataset and the algorithmic rules for determining the postoperative status of HER2 subtype breast cancer, generate positive and negative classification labels. The specific labeling rules are as follows: label a sample as positive if Her2 score is 3+ or Her2 score is 2+ and the postoperative FISH test result shows amplification; all other samples are labeled as negative.

搜集汇总

数据集介绍

特点

该数据集为山东省HER2型乳腺癌辅助诊断模型训练数据，包含150条记录，涵盖受试者、术后病理结果、Her2状态等信息，用于训练人工智能模型以提高诊断准确性。