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Additional file 5 of Logistic regression model training based on the approximate homomorphic encryption

Figshare2018-10-12 更新2026-04-29 收录

数据链接：

https://figshare.com/articles/dataset/Additional_file_5_of_Logistic_regression_model_training_based_on_the_approximate_homomorphic_encryption/7200134 数据链接链接失效反馈

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资源简介：

pcs. Prostate Cancer Study dataset (TXT 9 kb)

应用场景：

创建时间：

2018-10-12

前列腺癌预测

医学图像分析

To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients. A total of 2592 preoperative biopsy H

DataCite Commons2026-01-21 更新100

Data for Novel Approaches to Stability for Enhanced Privacy-Preserving Machine Learning

隐私保护机器学习

模型稳定性增强

Recently, machine learning models have seen considerable growth in size and popularity, lead-ing to concerns regarding dataset privacy, especially around sensitive data containing personal information

IEEE60

Multivariate model; PSA levels (ng/ml) the number of positive biopsy cores and PSA density (ng/ml) for predicting worse final pathological findings; Statistics: *Chi-Quadrat Pearson; **Fisher test.

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临床结果分析

Multivariate model; PSA levels (ng/ml) the number of positive biopsy cores and PSA density (ng/ml) for predicting worse final pathological findings; Statistics: *Chi-Quadrat Pearson; **Fisher test.

Figshare2015-12-03 更新40

放射组学预后

前列腺癌预测

This record contains raw data related to article “Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy" Abstract: Objective: The present study aimed to

NIAID Data Ecosystem80

Table_3_Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis.xlsx

前列腺癌预测

临床决策模型

ObjectiveThe aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the ini

NIAID Data Ecosystem50

Additional file 5 of Logistic regression model training based on the approximate homomorphic encryption

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