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 Dec 12, 2022
77 Dec 21, 2022
75 Dec 09, 2022
6 Dec 25, 2022
276 Dec 31, 2022
32 Dec 27, 2022
50 Dec 16, 2022
48 Dec 18, 2022
176 Dec 19, 2022
36 Nov 26, 2022
800 Dec 05, 2022
37 Nov 13, 2022
1.3k Dec 29, 2022
120 Dec 30, 2022
124 Dec 10, 2022
215 Dec 19, 2022
84 Dec 26, 2022
165 Dec 07, 2022
553 Dec 07, 2022
40 Nov 23, 2022