Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of ...
Use the decision tree for classification based on Breast cancer dataset available at https://www.kaggle.com/uciml/breast-cancer-wisconsin-data. 基于Python的可视化参考:DT可视化工具graphviz,python接口工具为pydotplus,需要提前安装graphviz并添加PATH到环境变量中,之后利用pydotplus可视化sklearn中的DT结果。http:/...
威斯康星州乳腺癌数据集是scikitlearm(skleam)库中-一个常用的内置数据集,用于分类任务。该数据集包含了从乳腺癌患者收集的肿瘤特征的测量值,以及相应的良性(benign)或恶性(malignant)标签。以下是对该数据集的简单介绍: 数据集名称:威斯康星州乳腺癌数据集(BreastCancerWisconsinDataset) 数据集来源:数据集最初由威斯康...
In the BreaKHis dataset, malignant samples significantly outnumber benign samples, creating an overall class imbalance that affects the model's performance and potentially leads to a bias toward the more dominant malignant class. Therefore, data augmentation is crucial for balancing breast cancer types ...
AI has gradually been used in the treatment decision support for breast cancer among oncologists with varying expertise22. Ha et al. developed a convolutional neural network algorithm to predict the molecular subtype of a breast cancer based on MRI features, and the test set accuracy was 70%, ...
Breast Cancer Dataset is provided by University of Wisconsin.本数据集由威斯康星大学提供。 数据列表 数据名称上传日期大小下载 breastcancer_unformatted-data2021-02-2720.86KB breastcancer_unformatted-data.data2021-02-2743.20KB breastcancer_wdbc.data2021-02-27121.19KB breastcancer_wdbc.names2021-02-274.60KB ...
In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data ac...
Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essentia
Tumour vascular density assessed from CD-31 immunohistochemistry (IHC) images has previously been shown to have prognostic value in breast cancer. Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer v
future if the sample size for the breast cancer radiogenomic study is large enough. Thirdly, there is no other publicly available breast cancer radiogenomic dataset which can be used to conduct this kind of radiogenomic experiments. Thus, the extracted features cannot be replicated in another ...