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+# 3DConvNet Solutions for Medical Image Challenges
+This repository contains 3d ConvNet Solutions for Medical Image Challenges.
+The project is based on  [Tencent MedicalNet](https://github.com/Tencent/MedicalNet) and [MONAI framework](https://monai.io/)
+which provides a series of 3D-ResNet pre-trained models and domain-optimized foundational capabilities for developing healthcare imaging training workflows.
+
+### Update(2020/05/01)
+I provide a baseline 3DConvNet code for TReNDS Neuroimaging challenge host on Kaggle. 
+
+### Contents
+1. [Requirements](#Requirements)
+2. [Code](#Demo)
+4. [Experiments](#Experiments)
+5. [TODO](#TODO)
+
+### Requirements
+- Python 3.7.0
+- PyTorch-1.5
+- monai-0.1.0  
+
+### Code
+- Structure of data directories base on MedicalNet
+```
+/
+    |--datasets/：Data preprocessing module
+    |   |--brains18.py：MRBrainS18 data preprocessing script
+    |   |--RSNA19.py：RSNA19 data preprocessing script
+    |   |--TReNDs.py：TReNDs data preprocessing script
+    |--models/：Model construction module
+    |   |--resnet.py：3D-ResNet network build script
+    |--utils/：tools
+    |   |--logger.py：Logging script
+    |--toy_data/：For CI test
+    |--data/：Data storage module
+    |   |--MRBrainS18/：MRBrainS18 dataset
+    |	|   |--images/：source image named with patient ID
+    |	|   |--labels/：mask named with patient ID
+    |   |--train.txt: training data lists
+    |   |--val.txt: validation data lists
+    |--pretrain/：Pre-trained models storage module
+    |--model.py: Network processing script
+    |--setting.py: Parameter setting script
+    |--train_MRBrainS18.py: MRBrainS18 training demo script
+    |--train_TReNDs.py: TReNDs training script
+    |--train_RSNA19.py
+    |--README.md
+```
+
+ Download data & pre-trained models from Tencent MedicalNet official repo ([Google Drive](https://drive.google.com/file/d/1399AsrYpQDi1vq6ciKRQkfknLsQQyigM/view?usp=sharing) or [Tencent Weiyun](https://share.weiyun.com/55sZyIx))
+ 
+- Network structure parameter settings
+```
+Model name   : parameters settings
+resnet_10.pth: --model resnet --model_depth 10 --resnet_shortcut B
+resnet_18.pth: --model resnet --model_depth 18 --resnet_shortcut A
+resnet_34.pth: --model resnet --model_depth 34 --resnet_shortcut A
+resnet_50.pth: --model resnet --model_depth 50 --resnet_shortcut B
+resnet_101.pth: --model resnet --model_depth 101 --resnet_shortcut B
+resnet_152.pth: --model resnet --model_depth 152 --resnet_shortcut B
+resnet_200.pth: --model resnet --model_depth 200 --resnet_shortcut B
+```
+
+### Baseline for TReNDS Neuroimaging challenge
+- 3D-Resnet10 trained from scratch [Pretrained models](https://drive.google.com/open?id=1mB59NoADt0n4yC-MviMtBUcYCE2YWJZz)
+
+Network | fold 0| fold 1| fold 2| fold 3| fold 4|
+---|---|---|---|---|---|
+3D-Resnet10 Train from scratch|0.1700|0.1685|0.1729|0.1734|0.1734
+3D-Resnet10 MedicalNet pretrained|0.1694|0.1691|0.1726|0.1746|0.1734
+
+### Computational Cost 
+```
+GPU：NVIDIA Tesla P40
+```
+<table class="dataintable">
+<tr>
+   <th class="dataintable">Network</th>
+   <th>Paramerers (M)</th>
+   <th>Running time (s)</th>
+</tr>
+<tr>
+   <td>3D-ResNet10</td>
+   <td>14.36</td>
+   <td>0.18</td>
+</tr class="dataintable">
+<tr>
+   <td>3D-ResNet18</td>
+   <td>32.99</td>
+   <td>0.19</td>
+</tr>
+<tr>
+   <td>3D-ResNet34</td>
+   <td>63.31</td>
+   <td>0.22</td>
+</tr>
+<tr>
+   <td>3D-ResNet50</td>
+   <td>46.21</td>
+   <td>0.21</td>
+</tr>
+<tr>
+   <td>3D-ResNet101</td>
+   <td>85.31</td>
+   <td>0.29</td>
+</tr>
+<tr>
+   <td>3D-ResNet152</td>
+   <td>117.51</td>
+   <td>0.34</td>
+</tr>
+<tr>
+   <td>3D-ResNet200</td>
+   <td>126.74</td>
+   <td>0.45</td>
+</tr>
+</table>
+
+- Please refer to [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625) for more details：
+
+### TODO
+- [x] Baseline (pure 3D ConvNet) code for TReNDS Neuroimaging challenge
+- [ ] Code and pretrained models for Intracranial-Hemorrhage-Detection (RSNA2019 challenge dataset)
+- [ ] More baseline code and pretrained models for recent Medical Image Challenges
+