3D CT Scan Segmentation With Occupancy Network
This repo implements a 3D superresolution segmentation task for an airport baggage dataset.
Our final paper can be found here.
Model Architecture
To solve the problem of binary classification an Occupancy Network is utilize. The occupancy network is implemented with and without concatenation of the latent tensor from the encoding to the decoding path. Additionally conditional batch normalization in the decoding path can be utilized.
Original occupancy network with conditional batch normalization and without concatenation 
Occupancy network without conditional batch normalization and with concatenation
Usage
The occupancy network can be trained and/or tested, on the airport baggage dataset, by simply executing the main file.
python main.py
The following arguments can be passed to the main.py script.
| Argument | Default value | Info |
|---|---|---|
--train |
1 (True) | Flag to perform training |
--test |
1 (True) | Flag to perform testing |
--batch_size |
10 | Batch size to be utilized |
--lr |
1e-04 | Learning rate to use |
--gpus_to_use |
'0' | Indexes of the GPUs to be use |
--use_data_parallel |
0 (False) | Use multiple GPUs (num of GPUs must be a factor of the batch size) |
--epochs |
100 | Epochs to perform while training |
--use_cat |
1 (True) | One if concatenation should be utilized |
--use_cbn |
1 (True) | One if conditional BN should be utilized else normal BN is used |
--loss |
'cross_entropy' | Loss function to be utilized ('cross_entropy', 'dice' or 'focal') |
--load_model |
'None' | Path to model to be loaded |
Results
9.7k Jan 01, 2023
0 Jan 20, 2022
48 Dec 20, 2022
17 Dec 23, 2022
57 Dec 27, 2022
2 Aug 19, 2022
8 May 22, 2022
205 Jan 09, 2023
4 Sep 09, 2022
3 Oct 01, 2022
185 Dec 26, 2022
147 Dec 05, 2022
77 Dec 16, 2022
80 Dec 13, 2022
11 Dec 13, 2022
268 Dec 22, 2022
46 Jan 01, 2023
69 Dec 10, 2022
688 Jan 06, 2023
5 Dec 29, 2022