ResNet-152
收藏kaggle2017-12-13 更新2024-03-07 收录
下载链接：
https://www.kaggle.com/datasets/pytorch/resnet152
下载链接
链接失效反馈官方服务：
资源简介：
ResNet-152 Pre-trained Model for PyTorch
应用场景：
创建时间：
2017-12-13
相关数据集
ResNet-152 cross-validation results.
Objectives The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced b
NIAID Data Ecosystem00
Single-domain results for fine-tuned ResNet-152 models.
B = 1000, α = 0.05. Unless explicitly stated, pempir ≤ 10−3 for all significance tests prior to correction for multiple comparisons, and padj ≤ 1.1 ⋅ 10−3 for all significance tests after adjustment f
NIAID Data Ecosystem00
The classification results of ResNet-152 model.
Objectives The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced b
NIAID Data Ecosystem00
Single-domain results for fine-tuned ResNet-152 models.
B = 1000, α = 0.05. Unless explicitly stated, pempir ≤ 10−3 for all significance tests prior to correction for multiple comparisons, and padj ≤ 1.1 ⋅ 10−3 for all significance tests after adjustment f
Figshare2023-08-03 更新00
