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
36 Oct 30, 2022
10 Dec 10, 2022
44 Nov 29, 2022
3 Jan 30, 2022
127 Jan 04, 2023
14 Nov 29, 2022
55 Jun 15, 2022
1 Jan 15, 2022
114 Nov 30, 2022
46 Dec 26, 2022
168 Oct 20, 2022
3 Oct 31, 2021
1 Dec 04, 2021
1.2k Dec 30, 2022
1 Jan 18, 2022
7 May 25, 2022
75 Dec 14, 2022
20 May 28, 2022
36 Oct 19, 2022
129 Dec 15, 2022