---
tags:
- monai
- medical
library_name: monai
license: apache-2.0
---
# Description
A neural architecture search algorithm for volumetric (3D) segmentation of the pancreas and pancreatic tumor from CT image.

# Model Overview
This model is trained using the state-of-the-art algorithm [1] of the "Medical Segmentation Decathlon Challenge 2018" with 196 training images, 56 validation images, and 28 testing images.

## Data
The training dataset is Task07_Pancreas.tar from http://medicaldecathlon.com/. And the data list/split can be created with the script `scripts/prepare_datalist.py`.

## Training configuration
The training was performed with at least 16GB-memory GPUs.

Actual Model Input: 96 x 96 x 96

## Input and output formats
Input: 1 channel CT image

Output: 3 channels: Label 2: pancreatic tumor; Label 1: pancreas; Label 0: everything else

## Scores
This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.72

## commands example
Create data split (.json file):

```
python scripts/prepare_datalist.py --path /path-to-Task07_Pancreas/ --output configs/dataset_0.json
```

Execute model searching:

```
python -m scripts.search run --config_file configs/search.yaml
```

Execute multi-GPU model searching (recommended):

```
torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml
```

Execute training:

```
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.yaml --logging_file configs/logging.conf
```

Override the `train` config to execute multi-GPU training:

```
torchrun --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" --logging_file configs/logging.conf
```

Override the `train` config to execute evaluation with the trained model:

```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/evaluate.yaml']" --logging_file configs/logging.conf
```

Execute inference:

```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.yaml --logging_file configs/logging.conf
```

# Disclaimer
This is an example, not to be used for diagnostic purposes.

# References
[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).

# License
Copyright (c) MONAI Consortium

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.