SSL4MIS:用于医学图像分割的半监督学习,文献综述和代码实现的集合-源码

上传者: 42122988 | 上传时间: 2021-09-07 15:10:50 | 文件大小: 114KB | 文件类型: ZIP
医学图像分割的半监督学习。 近来,半监督图像分割已成为医学图像计算中的热门话题,不幸的是,由于隐私策略等原因,只有少数开源代码和数据集。为了便于评估和公平比较,我们正在尝试建立一个半监督医学图像分割基准,以促进医学影像计算社区中的半监督学习研究。如果您有兴趣,可以随时将实现或想法推送到此存储库。 该项目最初是为我们以前的工作开发的,如果您发现对您的研究有用,请考虑引用以下内容: @article{luo2020urpc, title={Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency}, author={Luo, Xiangde and Liao, Wen

文件下载

资源详情

[{"title":"( 59 个子文件 114KB ) SSL4MIS:用于医学图像分割的半监督学习,文献综述和代码实现的集合-源码","children":[{"title":"SSL4MIS-master","children":[{"title":"code","children":[{"title":"train_entropy_minimization_2D.py <span style='color:#111;'> 10.84KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"util.py <span style='color:#111;'> 4.65KB </span>","children":null,"spread":false},{"title":"ramps.py <span style='color:#111;'> 1.29KB </span>","children":null,"spread":false},{"title":"metrics.py <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"losses.py <span style='color:#111;'> 6.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"train_fully_supervised_2D.py <span style='color:#111;'> 9.00KB </span>","children":null,"spread":false},{"title":"val_3D.py <span style='color:#111;'> 3.98KB </span>","children":null,"spread":false},{"title":"train_uncertainty_rectified_pyramid_consistency_2D.py <span style='color:#111;'> 13.76KB </span>","children":null,"spread":false},{"title":"test_3D.py <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"test_3D_util.py <span style='color:#111;'> 5.87KB </span>","children":null,"spread":false},{"title":"dataloaders","children":[{"title":"acdc_data_processing.py <span style='color:#111;'> 1.32KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 6.57KB </span>","children":null,"spread":false},{"title":"dataset.py <span style='color:#111;'> 4.81KB </span>","children":null,"spread":false},{"title":"brats_proprecessing.py <span style='color:#111;'> 3.77KB </span>","children":null,"spread":false},{"title":"brats2019.py <span style='color:#111;'> 8.61KB </span>","children":null,"spread":false}],"spread":true},{"title":"train_mean_teacher_2D.py <span style='color:#111;'> 11.91KB </span>","children":null,"spread":false},{"title":"test_acdc_unet_semi_seg.sh <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"train_interpolation_consistency_training_3D.py <span style='color:#111;'> 12.24KB </span>","children":null,"spread":false},{"title":"train_adversarial_network_3D.py <span style='color:#111;'> 11.33KB </span>","children":null,"spread":false},{"title":"train_mean_teacher_3D.py <span style='color:#111;'> 11.14KB </span>","children":null,"spread":false},{"title":"train_acdc_unet_semi_seg.sh <span style='color:#111;'> 1.28KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false},{"title":"train_interpolation_consistency_training_2D.py <span style='color:#111;'> 12.97KB </span>","children":null,"spread":false},{"title":"train_fully_supervised_3D.py <span style='color:#111;'> 8.45KB </span>","children":null,"spread":false},{"title":"train_uncertainty_aware_mean_teacher_3D.py <span style='color:#111;'> 12.30KB </span>","children":null,"spread":false},{"title":"train_uncertainty_aware_mean_teacher_2D.py <span style='color:#111;'> 12.97KB </span>","children":null,"spread":false},{"title":"networks","children":[{"title":"efficient_encoder.py <span style='color:#111;'> 17.23KB </span>","children":null,"spread":false},{"title":"networks_other.py <span style='color:#111;'> 19.73KB </span>","children":null,"spread":false},{"title":"encoder_tool.py <span style='color:#111;'> 6.61KB </span>","children":null,"spread":false},{"title":"grid_attention_layer.py <span style='color:#111;'> 16.23KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 17.71KB </span>","children":null,"spread":false},{"title":"net_factory.py <span style='color:#111;'> 936B </span>","children":null,"spread":false},{"title":"unet_3D.py <span style='color:#111;'> 3.54KB </span>","children":null,"spread":false},{"title":"unet.py <span style='color:#111;'> 11.85KB </span>","children":null,"spread":false},{"title":"net_factory_3d.py <span style='color:#111;'> 792B </span>","children":null,"spread":false},{"title":"attention_unet.py <span style='color:#111;'> 6.19KB </span>","children":null,"spread":false},{"title":"vnet.py <span style='color:#111;'> 9.32KB </span>","children":null,"spread":false},{"title":"VoxResNet.py <span style='color:#111;'> 3.55KB </span>","children":null,"spread":false},{"title":"efficientunet.py <span style='color:#111;'> 7.74KB </span>","children":null,"spread":false},{"title":"pnet.py <span style='color:#111;'> 4.10KB </span>","children":null,"spread":false},{"title":"enet.py <span style='color:#111;'> 22.39KB </span>","children":null,"spread":false},{"title":"discriminator.py <span style='color:#111;'> 3.06KB </span>","children":null,"spread":false},{"title":"attention.py <span style='color:#111;'> 3.03KB </span>","children":null,"spread":false}],"spread":false},{"title":"train_entropy_minimization_3D.py <span style='color:#111;'> 10.39KB </span>","children":null,"spread":false},{"title":"train_adversarial_network_2D.py <span style='color:#111;'> 11.79KB </span>","children":null,"spread":false},{"title":"val_2D.py <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false},{"title":"test_2D_fully.py <span style='color:#111;'> 4.31KB </span>","children":null,"spread":false}],"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 17.34KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"ACDC","children":[{"title":"train_slices.list <span style='color:#111;'> 34.72KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 548B </span>","children":null,"spread":false},{"title":"test.list <span style='color:#111;'> 760B </span>","children":null,"spread":false},{"title":"train.list <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"val.list <span style='color:#111;'> 380B </span>","children":null,"spread":false}],"spread":true},{"title":"BraTS2019","children":[{"title":"test.txt <span style='color:#111;'> 1.20KB </span>","children":null,"spread":false},{"title":"train.txt <span style='color:#111;'> 4.95KB </span>","children":null,"spread":false},{"title":"val.txt <span style='color:#111;'> 506B </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 307B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":".gitignore <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明