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---
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license: mit
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library_name: pytorch
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tags:
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- image-classification
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- explainable-ai
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- medical-imaging
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- grad-cam
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---
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# T1 MLOps Stack — ChestXplain Checkpoint
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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.
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## Training provenance
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- **Architecture:** DenseNet121 with a multi-label classification head
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- **Dataset:** NIH ChestX-ray14-derived training subset
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- **Training subset:** 20,000 images
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- **Checkpoint epoch:** 6
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- **Validation AUC:** 0.813 (recorded in the checkpoint metadata)
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- **Checkpoint SHA-256:** `5bf5e15396805ac85a5c1f1839813bc4ea3a66ff694f5ed6f12c1fd2eaee0cbf`
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- **Artifact:** `densenet121_chestxray.pth`
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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.
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## Intended use
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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.
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## Limitations
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- The model was trained on a 20,000-image subset rather than the complete NIH ChestX-ray14 dataset.
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- Dataset labels are noisy and may not represent definitive clinical diagnoses.
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- Performance may vary across institutions, scanners, patient populations, acquisition protocols, and disease prevalence.
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- Reported metrics are not evidence of clinical efficacy, safety, fairness, or regulatory approval.
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- Grad-CAM highlights model attribution regions; it is not a clinical explanation or proof of pathology.
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- The checkpoint has not undergone external validation, prospective evaluation, calibration analysis, or regulatory review.
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## License and provenance
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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:
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<https://github.com/ajinkya-awari/t1-mlops-stack>
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