Model Overview

A pre-trained model for volumetric (3D) detection of the lung nodule from CT image.

This model is trained on LUNA16 dataset (https://luna16.grand-challenge.org/Home/), using the RetinaNet (Lin, Tsung-Yi, et al. \"Focal loss for dense object detection.\" ICCV 2017. https://arxiv.org/abs/1708.02002).

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Data

The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/), which is based on LIDC-IDRI database [3,4,5].

LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location.

Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.

10-fold data splitting

We follow the official 10-fold data splitting from LUNA16 challenge and generate data split json files using the script from nnDetection.

Please download the resulted json files from https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/LUNA16_datasplit-20220615T233840Z-001.zip.

In these files, the values of \"box\" are the ground truth boxes in world coordinate.

Data resampling

The raw CT images in LUNA16 have various of voxel sizes. The first step is to resample them to the same voxel size.\nIn this model, we resampled them into 0.703125 x 0.703125 x 1.25 mm.

Please following the instruction in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection to do the resampling.

Data download

The mhd/raw original data can be downloaded from LUNA16. The DICOM original data can be downloaded from LIDC-IDRI database [3,4,5]. You will need to resample the original data to start training.

Alternatively, we provide resampled nifti images and a copy of original mhd/raw images from LUNA16 for users to download.

Training configuration

The training was performed with the following:

GPU: at least 16GB GPU memory, requires 32G when exporting TRT model
Actual Model Input: 192 x 192 x 80
AMP: True
Optimizer: Adam
Learning Rate: 1e-2
Loss: BCE loss and L1 loss

Input

1 channel

List of 3D CT patches

Output

In Training Mode: A dictionary of classification and box regression loss.

In Evaluation Mode: A list of dictionaries of predicted box, classification label, and classification score.

Performance

Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.852, mAR=0.998, AP(IoU=0.1)=0.858, AR(IoU=0.1)=1.0.

Please note that this bundle is non-deterministic because of the max pooling layer used in the network. Therefore, reproducing the training process may not get exactly the same performance.\nPlease refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.

Training Loss

$\"A$

Validation Accuracy

The validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.

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TensorRT speedup

The lung_nodule_ct_detection bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that when using the TensorRT model for inference, the force_sliding_window parameter in the inference.json file must be set to true. This ensures that the bundle uses the SlidingWindowInferer during inference and maintains the input spatial size of the network. Otherwise, if given an input with spatial size less than the infer_patch_size, the input spatial size of the network would be changed.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

method	torch_fp32(ms)	torch_amp(ms)	trt_fp32(ms)	trt_fp16(ms)	speedup amp	speedup fp32	speedup fp16	amp vs fp16
model computation	7449.84	996.08	976.67	626.90	7.63	7.63	11.88	1.56
end2end	36458.26	7259.35	6420.60	4698.34	5.02	5.68	7.76	1.55

Where:

model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2end means run the bundle end-to-end with the TensorRT based model.
torch_fp32 and torch_amp are for the PyTorch models with or without amp mode.
trt_fp32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup fp32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.

This result is benchmarked under:

TensorRT: 8.5.3+cuda11.8
Torch-TensorRT Version: 1.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.8.10
CUDA version: 12.0
GPU models and configuration: A100 80G

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute training:

python -m monai.bundle run --config_file configs/train.json\n

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

\n\">

python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>\n

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

python -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n

Execute inference on resampled LUNA16 images by setting `\"whether_raw_luna16\": false` in `inference.json`:

python -m monai.bundle run --config_file configs/inference.json\n

With the same command, we can execute inference on original LUNA16 images by setting \"whether_raw_luna16\": true in inference.json. Remember to also set \"data_list_file_path\": \"$@bundle_root + '/LUNA16_datasplit/mhd_original/dataset_fold0.json'\" and change \"dataset_dir\".

Note that in inference.json, the transform \"LoadImaged\" in \"preprocessing\" and \"AffineBoxToWorldCoordinated\" in \"postprocessing\" has \"affine_lps_to_ras\": true.\nThis depends on the input images. LUNA16 needs \"affine_lps_to_ras\": true.\nIt is possible that your inference dataset should set \"affine_lps_to_ras\": false.

Export checkpoint to TensorRT based models with fp32 or fp16 precision

--input_shape "[1, 1, 512, 512, 192]" --use_onnx "True" --use_trace "True" --onnx_output_names "['output_0', 'output_1', 'output_2', 'output_3', 'output_4', 'output_5']" --network_def#use_list_output "True"\n\">

python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision <fp32/fp16> --input_shape \"[1, 1, 512, 512, 192]\"  --use_onnx \"True\" --use_trace \"True\" --onnx_output_names \"['output_0', 'output_1', 'output_2', 'output_3', 'output_4', 'output_5']\" --network_def#use_list_output \"True\"\n

Execute inference with the TensorRT model

python -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n

References

[1] Lin, Tsung-Yi, et al. \"Focal loss for dense object detection.\" ICCV 2017. https://arxiv.org/abs/1708.02002)

[2] Baumgartner and Jaeger et al. \"nnDetection: A self-configuring method for medical object detection.\" MICCAI 2021. https://arxiv.org/pdf/2106.00817.pdf

[3] Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

[4] Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915--931, 2011. DOI: https://doi.org/10.1118/1.3528204

[5] Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

License

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

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

Unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.

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Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location.\n\nDisclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.\n\n#### 10-fold data splitting\n\nWe follow the official 10-fold data splitting from LUNA16 challenge and generate data split json files using the script from [nnDetection](https://github.com/MIC-DKFZ/nnDetection/blob/main/projects/Task016_Luna/scripts/prepare.py).\n\nPlease download the resulted json files from https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/LUNA16_datasplit-20220615T233840Z-001.zip.\n\nIn these files, the values of \"box\" are the ground truth boxes in world coordinate.\n\n#### Data resampling\n\nThe raw CT images in LUNA16 have various of voxel sizes. The first step is to resample them to the same voxel size.\nIn this model, we resampled them into 0.703125 x 0.703125 x 1.25 mm.\n\nPlease following the instruction in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection to do the resampling.\n\n#### Data download\n\nThe mhd/raw original data can be downloaded from [LUNA16](https://luna16.grand-challenge.org/Home/). The DICOM original data can be downloaded from [LIDC-IDRI database](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) [3,4,5]. You will need to resample the original data to start training.\n\nAlternatively, we provide [resampled nifti images](https://drive.google.com/drive/folders/1JozrufA1VIZWJIc5A1EMV3J4CNCYovKK?usp=share_link) and a copy of [original mhd/raw images](https://drive.google.com/drive/folders/1-enN4eNEnKmjltevKg3W2V-Aj0nriQWE?usp=share_link) from [LUNA16](https://luna16.grand-challenge.org/Home/) for users to download.\n\n### Training configuration\n\nThe training was performed with the following:\n\n- GPU: at least 16GB GPU memory, requires 32G when exporting TRT model\n- Actual Model Input: 192 x 192 x 80\n- AMP: True\n- Optimizer: Adam\n- Learning Rate: 1e-2\n- Loss: BCE loss and L1 loss\n\n#### Input\n\n1 channel\n- List of 3D CT patches\n\n#### Output\n\nIn Training Mode: A dictionary of classification and box regression loss.\n\nIn Evaluation Mode: A list of dictionaries of predicted box, classification label, and classification score.\n\n### Performance\n\nCoco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.852, mAR=0.998, AP(IoU=0.1)=0.858, AR(IoU=0.1)=1.0.\n\nPlease note that this bundle is non-deterministic because of the max pooling layer used in the network. Therefore, reproducing the training process may not get exactly the same performance.\nPlease refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.\n\n##### Training Loss\n\n![A graph showing the detection train loss](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_train_loss_v2.png)\n\n##### Validation Accuracy\n\nThe validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.\n\n![A graph showing the detection val accuracy](https://developer.download.nvidia.com/assets/Clara/Images/monai_retinanet_detection_val_acc_v2.png)\n\n##### TensorRT speedup\n\nThe `lung_nodule_ct_detection` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that when using the TensorRT model for inference, the `force_sliding_window` parameter in the `inference.json` file must be set to `true`. This ensures that the bundle uses the `SlidingWindowInferer` during inference and maintains the input spatial size of the network. Otherwise, if given an input with spatial size less than the `infer_patch_size`, the input spatial size of the network would be changed.\n\n| method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 7449.84 | 996.08 | 976.67 | 626.90 | 7.63 | 7.63 | 11.88 | 1.56 |\n| end2end | 36458.26 | 7259.35 | 6420.60 | 4698.34 | 5.02 | 5.68 | 7.76 | 1.55 |\n\nWhere:\n- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing\n- `end2end` means run the bundle end-to-end with the TensorRT based model.\n- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model\n- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.\n\nCurrently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.\n\nThis result is benchmarked under:\n- TensorRT: 8.5.3+cuda11.8\n- Torch-TensorRT Version: 1.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.8.10\n- CUDA version: 12.0\n- GPU models and configuration: A100 80G\n\n### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute training:\n\n```\npython -m monai.bundle run --config_file configs/train.json\n```\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n```\npython -m monai.bundle run --config_file configs/train.json --dataset_dir \n```\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Execute inference on resampled LUNA16 images by setting `\"whether_raw_luna16\": false` in `inference.json`:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\nWith the same command, we can execute inference on original LUNA16 images by setting `\"whether_raw_luna16\": true` in `inference.json`. Remember to also set `\"data_list_file_path\": \"$@bundle_root + '/LUNA16_datasplit/mhd_original/dataset_fold0.json'\"` and change `\"dataset_dir\"`.\n\nNote that in inference.json, the transform \"LoadImaged\" in \"preprocessing\" and \"AffineBoxToWorldCoordinated\" in \"postprocessing\" has `\"affine_lps_to_ras\": true`.\nThis depends on the input images. LUNA16 needs `\"affine_lps_to_ras\": true`.\nIt is possible that your inference dataset should set `\"affine_lps_to_ras\": false`.\n\n##### Export checkpoint to TensorRT based models with fp32 or fp16 precision\n\n```bash\npython -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision --input_shape \"[1, 1, 512, 512, 192]\" --use_onnx \"True\" --use_trace \"True\" --onnx_output_names \"['output_0', 'output_1', 'output_2', 'output_3', 'output_4', 'output_5']\" --network_def#use_list_output \"True\"\n```\n\n##### Execute inference with the TensorRT model\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n```\n\n## References\n\n[1] Lin, Tsung-Yi, et al. \"Focal loss for dense object detection.\" ICCV 2017. https://arxiv.org/abs/1708.02002)\n\n[2] Baumgartner and Jaeger et al. \"nnDetection: A self-configuring method for medical object detection.\" MICCAI 2021. https://arxiv.org/pdf/2106.00817.pdf\n\n[3] Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX\n\n[4] Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915--931, 2011. DOI: https://doi.org/10.1118/1.3528204\n\n[5] Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7\n\n## License\n\nCopyright (c) MONAI Consortium\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.","displayName",["D",1731434182503],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_lung_nodule_ct_detection","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[388,389,390,391,392,393,394,395,396,397,398,399],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_357":429,"_401":429,"_402":430,"_404":405},{"_357":427,"_401":427,"_402":428,"_404":405},{"_357":425,"_401":425,"_402":426,"_404":405},{"_357":423,"_401":423,"_402":424,"_404":405},{"_357":419,"_401":420,"_402":421,"_404":405},{"_357":417,"_401":417,"_402":418,"_404":405},{"_357":415,"_401":415,"_402":416,"_404":405},{"_357":413,"_401":413,"_402":414,"_404":405},{"_357":411,"_401":411,"_402":412,"_404":405},{"_357":409,"_401":409,"_402":410,"_404":405},{"_357":407,"_401":407,"_402":408,"_404":405},{"_357":400,"_401":400,"_402":403,"_404":405},"Pytorch","value","filter",{"_406":405,"_401":400},"category","label","field","NSPECT-SF7Z-5R4J",{"_406":405,"_401":407},"MONAI",{"_406":405,"_401":409},"Medical Imaging",{"_406":405,"_401":411},"Medical",{"_406":405,"_401":413},"Lung",{"_406":405,"_401":415},"Imaging",{"_406":405,"_401":417},"Healthcare","indus_healthcare",{"_406":422,"_401":420},"industry","Detection",{"_406":405,"_401":423},"Deep Learning",{"_406":405,"_401":425},"CT",{"_406":405,"_401":427},"Bundle",{"_406":405,"_401":429},{"_248":1605,"_250":1606},"ngcApps",[1558],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1539":1540,"_1541":1542,"_1543":1544,"_1545":1546,"_1547":442,"_279":466,"_1548":1549,"_1550":56,"_1551":227,"_1552":227,"_1553":1554,"_1555":227,"_1556":1557},"amplitude",{"_1536":1537,"_1138":-7,"_1538":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1530":1531,"_1532":1533,"_1534":1535},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1413":309,"_1414":1415,"_1416":1417,"_1418":1419},"announcements",[],"consentState",{"_504":1403,"_1404":227,"_1405":31,"_1406":31,"_1407":1408},"consentConfig",{"_486":487,"_488":489,"_490":491,"_492":493,"_494":495,"_496":497,"_498":499,"_500":501,"_502":503,"_504":505},"showContactAdmin",1784200425041,[],[],[480,481,482,483,484,485],{"_45":60,"_47":71,"_49":243,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":243,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":243,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":243,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":243,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":59,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688],[527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543],{"_544":650,"_357":650,"_308":309,"_518":651},{"_544":645,"_357":645,"_308":309,"_518":646},{"_544":641,"_357":641,"_308":309,"_518":642},{"_544":636,"_357":636,"_308":309,"_518":637},{"_544":631,"_357":631,"_308":309,"_518":632},{"_544":619,"_357":619,"_308":309,"_518":620},{"_544":607,"_357":607,"_308":309,"_518":608},{"_544":602,"_357":602,"_308":309,"_518":603},{"_544":591,"_357":591,"_308":309,"_518":592},{"_544":586,"_357":586,"_308":309,"_518":587},{"_544":581,"_357":581,"_308":309,"_518":582},{"_544":576,"_357":576,"_308":309,"_518":577},{"_544":571,"_357":571,"_308":309,"_518":572},{"_544":566,"_357":566,"_308":309,"_518":567},{"_544":561,"_357":561,"_308":309,"_518":562},{"_544":554,"_357":554,"_308":309,"_518":555},{"_544":545,"_357":545,"_308":309,"_518":546},"hostName","amazon-adsystem.com",[547,548],{"_83":553,"_550":545,"_551":31,"_308":552},{"_83":549,"_550":545,"_551":31,"_308":552},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[556,557],{"_83":560,"_550":554,"_551":31,"_308":559},{"_83":558,"_550":554,"_551":31,"_308":559},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796],[741,742,743,744,745],{"_544":636,"_357":636,"_308":309,"_518":767},{"_544":602,"_357":602,"_308":309,"_518":762},{"_544":755,"_357":755,"_308":309,"_518":756},{"_544":751,"_357":751,"_308":309,"_518":752},{"_544":746,"_357":746,"_308":309,"_518":747},"nvcrm.my.site.com",[748],{"_83":749,"_550":746,"_551":31,"_308":750},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[753],{"_83":754,"_550":751,"_551":31,"_308":585},"edge","github.com",[757,758],{"_83":761,"_550":755,"_551":31,"_308":760},{"_83":759,"_550":755,"_551":31,"_308":760},"logged_in","This domain is owned by Github. 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LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download