CADe-CADx-evaluation

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CADe-CADx-evaluation

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This repository provides a complete evaluation of the performance of CADe (Computer-Aided Detection) and CADx (Computer-Aided Diagnosis) systems. It computes ROCs, Precison-Recall, FROCs (with severall Operating Points spacifictions), bootstrapps, Confidence Intervals, statistical comparison tests, across sets of detection predictions (...)

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# CADe/CADx Performance Evaluation Framework

[![Python](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/)
[![License](https://img.shields.io/github/license/EYONIS-AIDS-DS/CADe-CADx-evaluation)](https://raw.githubusercontent.com/EYONIS-AIDS-DS/CADe-CADx-evaluation/main/LICENSE)
[![arXiv](https://img.shields.io/badge/arXiv-2512.00281-b31b1b.svg)](https://arxiv.org/abs/2512.00281)
[![Documentation](https://readthedocs.org/projects/cade-cadx-evaluation/badge/?version=latest)](https://cade-cadx-evaluation.readthedocs.io/en/latest/)

This repository provides a complete, standardised performance evaluation framework for **CADe** (Computer-Aided Detection) and **CADx** (Computer-Aided Diagnosis) systems.  
It targets binary detection and classification systems (e.g. malignant nodule detection) and computes ROC curves, Precision-Recall curves, FROCs (with configurable operating points), bootstrap confidence intervals, and statistical comparison tests across arbitrary sets of predictions.

It serves two independent purposes:

1. **Reproduce** all results of the preprint *"Rethinking Lung Cancer Screening: AI Nodule Detection and Diagnosis Outperforms Radiologists, Leading Models, and Standards Beyond Size and Growth"* ([arXiv:2512.00281](https://arxiv.org/abs/2512.00281)).
2. **Provide** a reusable, generic evaluation pipeline for any CADe/CADx system on any dataset.

📖 **Full documentation:** [https://cade-cadx-evaluation.readthedocs.io/en/latest/](https://cade-cadx-evaluation.readthedocs.io/en/latest/)

📓 **Interactive tutorial:** [`tutorial_cade_cadx_evaluation.ipynb`](tutorial_cade_cadx_evaluation.ipynb) — a step-by-step Jupyter notebook covering installation, repository structure, one-command paper reproduction, and independent usage of the `evaluate_series`, `evaluate_lesions`, and `statistical_tests` modules on synthetic datasets.

---

## Table of Contents

1. [Installation](#1-installation)
   - 1.1 [uv — Package Manager & Git](#11-uv--package-manager--git)
   - 1.2 [Clone the Repository](#12-clone-the-repository)
   - 1.3 [Install Dependencies](#13-install-dependencies)
   - 1.4 [Install via pip (without cloning)](#14-install-via-pip-without-cloning)
   - 1.5 [Data Files Setup](#15-data-files-setup)
2. [Repository Structure](#2-repository-structure)
   - 2.1 [Configuration](#21-configuration)
   - 2.2 [Input Format](#22-input-format)
   - 2.3 [Modules evaluate_series & evaluate_lesions](#23-modules-evaluate_series--evaluate_lesions)
   - 2.4 [Module statistical_tests](#24-module-statistical_tests)
   - 2.5 [Outputs](#25-outputs)
3. [Reproduce All Paper Results](#3-reproduce-all-paper-results)
4. [Independent Submodule Usage](#4-independent-submodule-usage)
   - 4.1 [Submodule evaluate_series](#41-submodule-evaluate_series)
   - 4.2 [Submodule evaluate_lesions](#42-submodule-evaluate_lesions)
   - 4.3 [Submodule statistical_tests](#43-submodule-statistical_tests)
5. [Citation](#5-citation)

---

## 1. Installation

### 1.1 uv — Package Manager & Git

This repository uses **uv**, an ultra-fast Python package and environment manager (see the [complete uv installation guide](https://uv.pypa.io/en/stable/installation/)).  
If you do not have it yet, install it with:

**Linux / macOS:**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
# or, if curl is not available:
wget -qO- https://astral.sh/uv/install.sh | sh
```

**Windows (PowerShell):**
```powershell
irm https://astral.sh/uv/install.ps1 | iex
```

If you do not have **Git**, follow the [Git installation guide](https://git-scm.com/downloads).  
On Linux/macOS: `sudo apt-get install git`  
On Windows: download the [installer](https://git-scm.com/download/win) or use `choco install git`.

### 1.2 Clone the Repository

```bash
git clone https://github.com/EYONIS-AIDS-DS/CADe-CADx-evaluation.git
cd CADe-CADx-evaluation
```

Alternatively, download the ZIP from the GitHub *Code* button and unzip it.

### 1.3 Install Dependencies

Create the virtual environment and install all dependencies in one step:

```bash
uv venv
uv sync
```

### 1.4 Install via pip (without cloning)

If you only need the Python package (e.g. to use the evaluation functions in your own project), you can install directly from GitHub without cloning the repository:

```bash
pip install git+https://github.com/EYONIS-AIDS-DS/CADe-CADx-evaluation.git
```

> **Note:** this installs the package only — the `data/` directory and `config_paper.py` required to reproduce the paper figures are not included. Clone the full repository for that.

Activate the environment:

```bash
# Linux / macOS
source .venv/bin/activate

# Windows (PowerShell)
.venv\Scripts\activate
```

> **Python version:** the project requires Python >= 3.11, < 3.12. `uv` will download the correct interpreter automatically if needed.

### 1.5 Data Files Setup

Large CSV input files are stored via **Git LFS**. Pull them after cloning:

```bash
# Install Git LFS (once per machine)
# macOS:   brew install git-lfs
# Windows: choco install git-lfs  (or download from https://git-lfs.github.com/)
# Ubuntu:  sudo apt-get install git-lfs

git lfs install
git lfs pull
```

Verify the files were correctly downloaded (the first line should be a CSV header, not a Git LFS pointer):

```bash
head -1 data/data_lesions/lesions.csv
```

**If Git LFS is unavailable**, download the files manually (PowerShell):

```powershell
$files = @(
    "lesions.csv",
    "lesions_radiologists.csv",
    "lesions_longitudinal.csv",
    "lesions_model_CADe.csv",
    "lesions_nndetection_baumgartner_CADe.csv",
    "lesions_nndetection_CADe.csv",
    "lesions_nndetection_CADx.csv"
)
$base_url = "https://media.githubusercontent.com/media/EYONIS-AIDS-DS/CADe-CADx-evaluation/refs/heads/main/data/data_lesions"
foreach ($file in $files) {
    Invoke-WebRequest -Uri "$base_url/$file" -OutFile "data/data_lesions/$file"
}
```

---

## 2. Repository Structure

```
CADe-CADx-evaluation/
│
│   README.md
│   config_paper.py          <- all evaluation parameters for the paper
│   run_paper_evaluation.py  <- entry point to reproduce the full paper
│   pyproject.toml
│
├── data/
│   ├── log/                         (OUTPUT — created at runtime, git-ignored)
│   │       CADe_CADx_evaluate.log
│   │
│   ├── data_series/                 (INPUT)
│   │       series.csv
│   │
│   ├── data_lesions/                (INPUT)
│   │       lesions.csv
│   │       lesions_radiologists.csv
│   │       lesions_longitudinal.csv
│   │       lesions_model_CADe.csv
│   │       lesions_nndetection_CADe.csv
│   │       lesions_nndetection_CADex.csv
│   │       lesions_nndetection_baumgartner_CADe.csv
│   │
│   ├── evaluate_series/             (OUTPUT — created at runtime)
│   │   └── figure_1/
│   │       └── model_prediction_test1/
│   │               roc_curve_with_op_test1.png
│   │               roc_CI_bootstrap_hanley_test1.csv
│   │               operating_point_performances_test1.csv
│   │               AUC_array_5000_bootstrap_test1.npy
│   │               ...
│   │
│   ├── evaluate_lesions/            (OUTPUT — created at runtime)
│   │   └── figure_4/
│   │       └── model_prediction_test1/
│   │               froc_curve_with_2_op_test1.png
│   │               operating_point_FROC_scores_at_0.5_and_1_FP_per_scan_test1.csv
│   │               ...
│   │
│   └── statistical_tests/          (OUTPUT — created at runtime)
│       └── figure_1/
│               test_results_AUC_model_vs_4radiolog_test3.csv
│               distribution_BOOTSTRAPP_AUC_model_vs_4radiolog_test3.png
│               ...
│
└── CADe_CADx_evaluation/           (PACKAGE)
    ├── evaluate_common/
    │       roc.py
    │       roc_confidence_interval.py
    │       precision_recall.py
    │       sens_spec.py              <- sensitivity, specificity, accuracy, PPV, NPV
    │       dca.py                   <- Decision Curve Analysis (net benefit)
    │       sample_size_analysis.py
    │       plot_score_distribution_benign_cancer.py
    │       logger.py
    │
    ├── evaluate_series/
    │       evaluate_series.py
    │       plot_dca_comparison.py   <- DCA comparison figures
    │
    ├── evaluate_lesions/
    │       evaluate_lesions.py
    │       froc.py
    │       plot_diameter_prediction_distributions.py
    │
    └── statistical_tests/
            statistical_tests.py
```

### 2.1 Configuration

All parameters are centralised in `config_paper.py`. It defines:

- **Input / output paths** for data and results.
- **Prediction names** — column names in the CSV files corresponding to model or reader scores.
- **Label names** — column names for ground-truth binary labels.
- **Subset indicator names** — boolean columns that select evaluation subsets (e.g. `nlst_test_1`).
- **Bootstrap settings** — number of bootstrap samples (`nb_bootstrap_samples`) and confidence level (`confidence_threshold`).
- **Operating point thresholds and labels** — threshold values and human-readable names for each operating point on the ROC/FROC.
- **Fast computation flag** — when `fast_computation=True`, bootstrap samples are reduced to 50 and FROC resolution is lowered, allowing a full run in ~8 minutes instead of ~24 hours.
- **Evaluation lists** — lists of `(set_name, prediction, label, thresholds, labels)` tuples, grouped by figure.

To apply this package to a **new model or dataset**, either:
- Rewrite `config_paper.py` following the same structure (recommended for multi-figure evaluations), or
- Call the submodule functions directly (see [Section 4](#4-independent-submodule-usage)).

### 2.2 Input Format

Inputs are CSV files located in `data/data_series/` (patient/series level) and `data/data_lesions/` (nodule/lesion level). Each row is one sample; each column is a feature. The evaluation uses four types of columns:

| Type | Description | Examples |
|------|-------------|---------|
| **Predictions** | Numeric score (float) — model probability, reader score, or any predictive variable | `model_prediction`, `radiologist_1_max_malignancy`, `diameter_lad` |
| **Labels** | Binary ground-truth: `0` = negative, `1` = positive | `label`, `label_nodule` |
| **Identifiers** | Unique sample IDs used to compute per-scan FP rates | `series_uid`, `patient_id`, `detection_id` |
| **Subset indicators** | Boolean columns that define evaluation subsets | `nlst_test_1`, `nlst_test_3`, `radiologist_1` |

> **Lesion-level only:** an additional `detection_status` column is required, taking values `"TP"`, `"FP"`, or `"FN"` (True Positive, False Positive, False Negative) resulting from prior IoU-based pairing of detections with ground-truth lesions. False negative detections are assigned a prediction score of 0 (worst score) by the evaluation algorithm.

### 2.3 Modules `evaluate_series` & `evaluate_lesions`

These two modules perform the same computations at different levels:

- **`evaluate_series`** — patient/series level. Ground truth is straightforward (e.g. cancer diagnosis per scan).
- **`evaluate_lesions`** — nodule/lesion level. Requires prior detection–GT pairing (not included; see [Jaeger et al., nnDetection](https://arxiv.org/abs/1811.08661) for a standard IoU >= 0.1 pairing). Lesion-level evaluation additionally includes FROC curves.

Both modules call shared utilities from `evaluate_common`, which provides:
- ROC curves, Precision-Recall curves, bootstrap confidence intervals
- Sensitivity, specificity, accuracy at each operating point
- **PPV and NPV** (Positive/Negative Predictive Value) at each operating point
- **Decision Curve Analysis (DCA)** — net benefit sweep across probability thresholds, operating-point annotation, bootstrap CIs, and calibrated DCA for Lung-RADS comparison

### 2.4 Module `statistical_tests`

Implements non-parametric bootstrap-based superiority tests ([Efron & Tibshirani](https://www.hms.harvard.edu/bss/neuro/bornlab/nb204/statistics/bootstrap.pdf)) to compare pairs of models or readers.

**Input:** pairs of `.npy` bootstrap vector files produced by `evaluate_series` or `evaluate_lesions`.  
**Tests performed** (using [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)):
- One-sided Welch t-test (unequal variance) — H1: prediction 1 > prediction 2
- One-sided Student t-test (equal variance) — H1: prediction 1 > prediction 2

Supported bootstrap array types: **AUC**, **sensitivity**, **specificity**, and **net benefit (NB)** from DCA.

**Output per pair:** a CSV with p-values and significance labels (`extremely strong`, `very strong`, `strong`, `moderate`, `weak`, `no evidence`), plus a figure showing the two bootstrap distributions.

> This module must be run **after** `evaluate_series` / `evaluate_lesions` (and `run_dca_bootstrap.py` for DCA comparisons), as it depends on their `.npy` bootstrap output files.

### 2.5 Outputs

All outputs are written under `data/evaluate_series/`, `data/evaluate_lesions/`, and `data/statistical_tests/`, organised by figure name and analysis name.

**Figures** (saved as `.png` and `.svg` for vector editing):
- ROC curve with operating points (Youden index maximum by default, or any custom threshold list)
- Precision-Recall curve
- Distribution of malignancy predictions (benign vs. malignant)
- FROC curve with operating points at 0.5 FP/scan and 1 FP/scan *(lesion level only)*
- Bootstrap distribution comparison *(statistical tests only)*

**CSV files:**
- `sample_size_<set>.csv` — total, positive, and negative sample counts with imbalance ratio
- `roc_CI_bootstrap_hanley_<set>.csv` — AUC with bootstrap and [Hanley & McNeil](https://pubs.rsna.org/doi/10.1148/radiology.143.1.7063747) confidence intervals
- `operating_point_performances_<set>.csv` — sensitivity, specificity, accuracy with bootstrap mean and CI at each operating point
- `operating_point_FROC_scores_at_0.5_and_1_FP_per_scan_<set>.csv` — FROC sensitivity and FP/scan with CI *(lesion level only)*
- `test_results_<comparison>.csv` — p-values and significance conclusions *(statistical tests only)*

**NumPy arrays (`.npy`):**
- `AUC_array_5000_bootstrap_<set>.npy` — bootstrap AUC samples
- `sensitivity_array_5000_bootstrap_for_each_OP_ROC_<set>.npy` — bootstrap sensitivity samples per operating point
- `specificity_array_5000_bootstrap_for_each_OP_ROC_<set>.npy`
- `accuracy_array_5000_bootstrap_for_each_OP_ROC_<set>.npy`
- `at_0.5_and_1_FP_per_scansensitivity_array_5000bootstrap_for_each_OP_FROC_<set>.npy` *(lesion level only)*
- `NB_array_5000_bootstrap_for_each_OP_DCA_<set>.npy` — bootstrap net benefit samples per DCA operating point *(series level only)*

**DCA-specific files** (series level, written alongside ROC outputs):
- `dca_<set>.csv` — net benefit curve (probability threshold, NB model, NB treat-all, NB treat-none)
- `dca_operating_points_<set>.csv` — operating-point net benefit with bootstrap CI
- `dca_curve_with_op_<set>.{png,svg}` — DCA figure with operating-point markers
- `dca_comparison_*.{png,svg}` — side-by-side DCA comparison figures (model vs. comparators)

---

## 3. Reproduce All Paper Results

The `run_paper_evaluation.py` script integrates all evaluation modules and reproduces every figure, performance table, and statistical test in the paper in a single command.

**Fast run** (~8 minutes, 50 bootstrap samples — qualitatively equivalent):

```bash
# with uv
uv run python run_paper_evaluation.py --fast_computation=true

# or with the activated virtual environment
python run_paper_evaluation.py --fast_computation=true
```

**Full paper reproduction** (~24 hours, 5000 bootstrap samples):

```bash
uv run python run_paper_evaluation.py --fast_computation=false
```

**From a Python script:**

```python
from run_paper_evaluation import paper_evaluation

paper_evaluation(fast_computation=True)   # fast demo
# paper_evaluation(fast_computation=False)  # full paper reproduction
```

---

## 4. Independent Submodule Usage

The three submodules can be used independently on any dataset. The example below generates a synthetic CSV and evaluates it.

**Create a synthetic series CSV:**

```python
import numpy as np
import pandas as pd
from pathlib import Path

path_data_series = Path("data/data_series/series_example.csv")
sample_size = 1000
np.random.seed(42)

df = pd.DataFrame({
    "series_uid": np.arange(1, sample_size + 1),
    "label_col":  np.random.randint(0, 2, size=sample_size),
    "subset_col": np.random.choice([True, False], size=sample_size, p=[0.9, 0.1]),
})
# Make predictions correlated with labels
df["prediction_col"] = (
    ((df["label_col"] * 2) - 1) * np.random.rand(sample_size) * 0.5 + 0.5
)
df.to_csv(path_data_series, index=False)
```

### 4.1 Submodule `evaluate_series`

Evaluates model/reader performance at the **patient/series level**.

```python
from pathlib import Path
from CADe_CADx_evaluation.evaluate_series.evaluate_series import evaluate_serie_main

evaluate_serie_main(
    path_to_load_csv_serie=Path("data/data_series/series_example.csv"),
    expdir=Path("data/evaluate_series/my_experiment"),
    set_name="subset_col",                  # boolean column defining the evaluation subset
    prediction="prediction_col",            # numeric score column
    label_name="label_col",                 # binary ground-truth column (0/1)
    operating_point_thresholds=[0],         # 0 = Youden Index Maximum; or e.g. [0.3, 0.5, 0]
    operating_point_labels=["Youden Max"],  # human-readable label per threshold
    nb_bootstrap_samples=500,
    confidence_threshold=0.95,
)
```

All outputs are saved under `data/evaluate_series/my_experiment/<prediction_col>_<subset_col>/` and include:
- ROC and Precision-Recall curves
- Sensitivity, specificity, **PPV, NPV**, and accuracy at each operating point
- **DCA curve** with net benefit sweep and operating-point annotation
- Bootstrap `.npy` arrays for AUC, sensitivity, specificity, and **net benefit**

### 4.2 Submodule `evaluate_lesions`

Evaluates model/reader performance at the **nodule/lesion level**, including FROC curves.

> **Prerequisite:** the input CSV must contain a `detection_status` column (`"TP"`, `"FP"`, `"FN"`) and a `patient_id` column (used to compute FP per scan). Detection–GT pairing must be performed externally before calling this module (e.g. using [nnDetection](https://github.com/MIC-DKFZ/nnDetection) with IoU >= 0.1).

```python
from pathlib import Path
from CADe_CADx_evaluation.evaluate_lesions.evaluate_lesions import evaluate_lesions_main

evaluate_lesions_main(
    fast_computation=True,                    # True: 1 000-point FROC grid; False: 500 000-point grid
    path_to_load_csv_lesion=Path("data/data_lesions/lesions.csv"),
    expdir=Path("data/evaluate_lesions/my_experiment"),
    set_name="nlst_test_1",                   # boolean subset column, or any set_name (uses full df if unknown)
    prediction="model_prediction",            # numeric score column
    label_name="label",                       # binary ground-truth column (0/1)
    operating_point_thresholds=[0],           # 0 = Youden Index Maximum
    operating_point_labels=["Youden Max"],
    nb_bootstrap_samples=500,
    confidence_threshold=0.95,
)
```

Outputs are saved under `data/evaluate_lesions/my_experiment/<prediction>_<set_name>/` and include
everything from `evaluate_series` plus FROC figures and FROC operating-point CSV files.

### 4.3 Submodule `statistical_tests`

Performs bootstrap-based superiority tests between pairs of models or readers.

> **Prerequisite:** `.npy` bootstrap files must already exist, produced by `evaluate_serie_main` or `evaluate_lesions_main`.

```python
from pathlib import Path
from CADe_CADx_evaluation.statistical_tests.statistical_tests import statistical_test_main

root = Path("data/evaluate_series/my_experiment")

# Each tuple: (comparison_name, path_to_bootstrap_array_1, path_to_bootstrap_array_2)
# H1: array_1 > array_2  (one-sided superiority test)
comparisons = [
    (
        "AUC_model_vs_reader",
        root / "model_prediction_test1" / "AUC_array_5000_bootstrap_test1.npy",
        root / "reader_prediction_test1" / "AUC_array_5000_bootstrap_test1.npy",
    ),
    (
        "SENS_model_vs_reader",
        root / "model_prediction_test1" / "sensitivity_array_5000_bootstrap_for_each_OP_ROC_test1.npy",
        root / "reader_prediction_test1" / "sensitivity_array_5000_bootstrap_for_each_OP_ROC_test1.npy",
    ),
]

statistical_test_main(
    list_of_tuples_of_pairs_of_bootstrap_paths=comparisons,
    expdir_analysis=Path("data/statistical_tests/my_experiment"),
)
```

For each comparison the module saves:
- `test_results_<comparison_name>.csv` — p-values and significance conclusions for both equal- and unequal-variance t-tests.
- `distribution_BOOTSTRAPP_<comparison_name>.png/.svg` — overlaid histogram of the two bootstrap distributions.

When the input arrays are 2-D (one column per operating point), one test and one figure are produced per operating point.

> **DCA net benefit comparisons:** to compare net benefit bootstrap arrays (produced by `run_dca_bootstrap.py`), pass `NB_array_*_bootstrap_for_each_OP_DCA_*.npy` files as the two paths — the same API applies.

---

## 5. Citation

If you use this repository in your research, please cite the associated preprint:

```bibtex
@misc{baudot2024rethinking,
  title         = {Rethinking Lung Cancer Screening: AI Nodule Detection and Diagnosis
                   Outperforms Radiologists, Leading Models, and Standards Beyond Size and Growth},
  author        = {Pierre Baudot and others},
  year          = {2024},
  eprint        = {2512.00281},
  archivePrefix = {arXiv},
  primaryClass  = {eess.IV},
  url           = {https://arxiv.org/abs/2512.00281}
}
```

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