VICTRE_PreliminaryDataCollection_MassCalc_DM_DBT

Name: VICTRE_PreliminaryDataCollection_MassCalc_DM_DBT
Creator: University Of Arizona, University Of Central Florida; University of Arizona, University of Central Floriday
Published: 2024-01-01T00:00:00+08:00

ICPSR2024-01-01 更新2026-04-16 收录

乳腺影像分析

肿块钙化检测

数据链接：

https://www.openicpsr.org/openicpsr/project/199187/version/V1/view 数据链接链接失效反馈

官方服务：

资源简介：

Deidentified Sample Data from Victre Breast Simulation for masses and calcs DM virtual image

提供机构：

University Of Arizona, University Of Central Florida; University of Arizona, University of Central Floriday

创建时间：

2024-01-01

bus_cot_preprocessed

乳腺影像分析

计算机辅助诊断

该数据集是一个医学影像数据集，专门用于乳腺影像分析任务。数据集包含6440个训练样本，总大小约648MB。每个样本包含以下核心字段：原始医学图像（image字段）、对应的分割掩码图像（mask字段）、图像文件路径（image_file和mask_file）、图像模态信息（imagemodality）、BI-RADS分类标签（birads）以及推理响应文本（reasoning_response）。数

Hugging Face2026-05-18 更新240

Digital mammography Dataset for Breast Cancer Diagnosis Research (DMID)

乳腺影像分析

影像诊断

This dataset contains images of mammograms and can be used for research and education purposes only. The dataset contains DCM images, TIFF images, a Radiology report, a Segmented mask, and pixe

Figshare2023-11-08 更新50

GAN-based data augmentation to improve breast Ultrasound and Mammography Mass Classification

乳腺影像分析

生成对抗网络数据增强

Various GAN methods, including Wasserstein GAN with Gradient Penalty (WGAN-GP), Cycle GAN, Conditional GAN, and Spectral Normalization GAN (SNGAN), were tested for data augmentation in breast regions

Mendeley Data30

Kinetic patterns of DCE-mammogram between benign and malignant breast lesions using 2–4 and 2–10 min time intervals (reader C).

乳腺影像分析

病灶动力学鉴别

Kinetic patterns of DCE-mammogram between benign and malignant breast lesions using 2–4 and 2–10 min time intervals (reader C).

NIAID Data Ecosystem70

Table_5_Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies.docx

乳腺影像分析

放射学诊断

ObjectivesTo investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast l

NIAID Data Ecosystem30

VICTRE_PreliminaryDataCollection_MassCalc_DM_DBT

资源简介：

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