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license: mit
library_name: pytorch
tags:
- image-classification
- explainable-ai
- medical-imaging
- grad-cam
---
# T1 MLOps Stack — ChestXplain Checkpoint
This repository contains the approved DenseNet121 checkpoint used by the ChestXplain research/demo application. The application performs multi-label classification of 14 thoracic conditions and provides Grad-CAM visual explanations.
## Training provenance
- **Architecture:** DenseNet121 with a multi-label classification head
- **Dataset:** NIH ChestX-ray14-derived training subset
- **Training subset:** 20,000 images
- **Checkpoint epoch:** 6
- **Validation AUC:** 0.813 (recorded in the checkpoint metadata)
- **Checkpoint SHA-256:** `5bf5e15396805ac85a5c1f1839813bc4ea3a66ff694f5ed6f12c1fd2eaee0cbf`
- **Artifact:** `densenet121_chestxray.pth`
The checkpoint is provided as a model artifact for the associated portfolio MLOps demonstration. NIH images and source data are not included in this repository.
## Intended use
Research, engineering evaluation, and educational/demo use only. This artifact is not validated for clinical deployment and must not be used to diagnose, treat, or make decisions about patients.
## Limitations
- The model was trained on a 20,000-image subset rather than the complete NIH ChestX-ray14 dataset.
- Dataset labels are noisy and may not represent definitive clinical diagnoses.
- Performance may vary across institutions, scanners, patient populations, acquisition protocols, and disease prevalence.
- Reported metrics are not evidence of clinical efficacy, safety, fairness, or regulatory approval.
- Grad-CAM highlights model attribution regions; it is not a clinical explanation or proof of pathology.
- The checkpoint has not undergone external validation, prospective evaluation, calibration analysis, or regulatory review.
## License and provenance
The model artifact is released under the repository's MIT license for research/demo purposes, subject to the terms and attribution requirements of the underlying NIH ChestX-ray14 dataset and its original publication. See the associated source project for inference code, evaluation context, and full citations:
<https://github.com/ajinkya-awari/t1-mlops-stack>
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