Abstract
This software offers methods and functions for training calibrated out-of-distribution detectors for medical
image classification tasks. It includes functionalities for training, synthesizing data augmentations,
calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with
standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.
- Developers:
-
Narayanaswamy, Vivek [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2023-06-05
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 108775
- Site Accession Number:
- LLNL-CODE-850636
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Narayanaswamy, Vivek S.
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors.
Computer Software.
https://github.com/LLNL/OODmedic.
USDOE National Nuclear Security Administration (NNSA).
05 Jun. 2023.
Web.
doi:10.11578/dc.20230627.1.
Narayanaswamy, Vivek S.
(2023, June 05).
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors.
[Computer software].
https://github.com/LLNL/OODmedic.
https://doi.org/10.11578/dc.20230627.1.
Narayanaswamy, Vivek S.
"Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors." Computer software.
June 05, 2023.
https://github.com/LLNL/OODmedic.
https://doi.org/10.11578/dc.20230627.1.
@misc{
doecode_108775,
title = {Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors},
author = {Narayanaswamy, Vivek S.},
abstractNote = {This software offers methods and functions for training calibrated out-of-distribution detectors for medical
image classification tasks. It includes functionalities for training, synthesizing data augmentations,
calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with
standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.},
doi = {10.11578/dc.20230627.1},
url = {https://doi.org/10.11578/dc.20230627.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230627.1}},
year = {2023},
month = {jun}
}