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
44 Nov 29, 2022
3 Dec 05, 2022
1 Jan 24, 2022
74 Dec 28, 2022
363 Dec 25, 2022
6 Jul 18, 2022
1.7k Jan 01, 2023
193 Dec 26, 2022
64 Jan 07, 2023
46 Dec 12, 2022
259 Dec 25, 2022
2 Jan 16, 2022
20 Jun 21, 2022
1.4k Dec 22, 2022
16 Dec 26, 2022
3 Sep 07, 2022
196 Jan 05, 2023
0 Jan 05, 2022
9 Dec 21, 2022
31 Dec 11, 2022