arnab39/FewShot_GAN-Unet3D：我们论文的张量流实现：使用生成对抗学习的少数三维多模态医学图

项目介绍

arnab39/FewShot_GAN-Unet3D 是一个托管在 GitHub 上的开源项目，主要使用 Python 编写，采用 MIT License 许可证，目前获得 400 个 Star、80 次 Fork。

仓库全名: arnab39/FewShot_GAN-Unet3D

README 摘要

Few-shot 3D Medical Image Segmentation using Generative Adversarial Learning This repository contains the tensorflow and pytorch implementation of the model we proposed in our paper of the same name: Few-shot 3D Medical Image Segmentation using Generative Adversarial Learning The code is available in both tensorflow and pytorch. To run the project kindly refer to the individual readme file. Dataset iSEG 2017 dataset…

完整内容见 GitHub 原始 README 或本页。

常见问题

FewShot_GAN-Unet3D 用什么语言开发？

主要使用 Python 编写，采用 MIT License 许可证，目前有 400 个 Star。

FewShot_GAN-Unet3D 可以免费商用吗？

如何获取 FewShot_GAN-Unet3D 的源码？

可以直接克隆仓库：git clone https://github.com/arnab39/FewShot_GAN-Unet3D.git，默认分支为 master；安装与运行步骤见仓库 README。

FewShot_GAN-Unet3D 最近还在维护吗？

仓库最近一次更新为 2020年10月28日，当前有 18 个未关闭 Issue；Star 数为 400，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

3d-gan brain-mri convolutional-neural-networks few-shot-learning gans image-segmentation iseg-challenge medical-image-analysis multi-modal-imaging semi-supervised-gan semi-supervised-learning tensorflow unet-3d unet-tensorflow weightnorm-3d

MIT License
400 Stars
80 Forks
10 Watching

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

570

价值分

264

活跃状态

活跃

GitHub

语言占比

Python

Shell

贡献者

arnab39

aakash-garg

Few-shot 3D Medical Image Segmentation using Generative Adversarial Learning

This repository contains the tensorflow and pytorch implementation of the model we proposed in our paper of the same name: Few-shot 3D Medical Image Segmentation using Generative Adversarial Learning

The code is available in both tensorflow and pytorch. To run the project kindly refer to the individual readme file.

Dataset

iSEG 2017 dataset was chosen to substantiate our proposed method. It contains the 3D multi-modal brain MRI data of 10 labeled training subjects and 13 unlabeled testing subjects. We split the 10 labeled training data into training, validation and testing images for both the models.(Eg- 2,1 and 7) Rest of the 13 unlabeled testing images are only used for training the GAN based model.

MRBrainS13 dataset was also used to test the robustness of our proposed model. It contains 3 modalities (T1-weighted, T1-weighted inversion recovery and FLAIR). Original Dataset consists of 5 labeled training and 6 unlabeled testing subjects. We split the 5 labeled training data into 1,1,3 for training, validation and testing. The 6 testing subjects are used as unlabeled data for the GAN based models.

Proposed model architecture

The following shows the model architecture of the proposed model. (Read our paper for further details)

Some results from our paper

Visual comparison of the segmentation by each model, for two test subjects of the iSEG-2017 dataset, when training with different numbers of labeled examples.

Segmentation of Subject 10 of the iSEG-2017 dataset predicted by different GAN-based models, when trained with 2 labeled images. The red box highlights a region in the ground truth where all these models give noticeable differences.
More such results can be found in the paper.

Contact

You can mail us at: [email protected], [email protected] If you use this code for your research, please consider citing the original paper:

Arnab Kumar Mondal, Jose Dolz, Christian Desrosiers. Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning.

暂无评论

FewShot_GAN-Unet3D