---
license: apache-2.0
tags:
- medical-imaging
- model-reliability
- calibration
- robustness
- cancer-imaging
---

# FIDELIS — Reliability Estimators

Model artifacts for **FIDELIS**, an open-source tool that evaluates the **calibration** and **robustness**
of trained deep-learning models in cancer imaging. FIDELIS operates **post-hoc** on a model's outputs — no
retraining, no access to the original training data — and returns a clinician-facing reliability report.

- Code: https://github.com/ZhixiangWang-CN/FIDELIS
- Program: NCI ITCR (RFA-CA-27-019, U01)

## Components (to be released)
- **FIDELIS-Calibrate** — post-hoc calibration assessment (Anatomy-Aware ECE / ΔA-ECE) and recalibration maps.
- **FIDELIS-Robust** — stability under simulated scanner / dose / protocol variation.
- **Clinical Decision Impact (CDI)** — maps reliability failures to potential changes in a clinical decision.

## Status
Repository reserved during early development; trained components and full **model cards** will be uploaded
and version-tagged with each release. Not a diagnostic device; not for autonomous clinical decision-making.

## License
Code components: Apache-2.0. Standalone model weights: CC-BY-4.0.