dicleak
Dataset integrity auditor for medical-imaging AI. Point it at a dataset manifest CSV and it catches the flaws that sink models and papers before you train: patient-level data leakage across splits, single-class splits, broken stratification, tiny test sets, duplicate files, un-deidentified IDs.
医学影像 AI 数据集完整性审计工具。对 manifest CSV 跑一遍，在训练之前 揪出致命缺陷：病人级数据泄露、单类别数据划分、分布失衡、测试集过小、 文件重复、未脱敏的 ID。
Install
pip install dicleak
Usage
dicleak splits.csv
dicleak splits.csv --report audit.md
dicleak splits.csv --report audit.md --cta "https://your-service.example/audit"
The manifest CSV needs three columns (headers are flexible):
| concept | accepted headers |
|---|---|
| patient id | patient_id, patientid, patient, subject_id, subject, pid |
| split | split, subset, fold, partition, set (train/val/test and common aliases) |
| label | label, class, target, diagnosis, annotation |
| file path (optional) | filepath, file_path, path, filename |
Exit code is 0 when the score is 60+, 1 otherwise — handy for CI gates.
The 7 checks
| # | check | severity |
|---|---|---|
| 1 | patient-level leakage across splits | CRITICAL |
| 2 | split containing a single class | CRITICAL |
| 3 | label distribution drift between splits | HIGH |
| 4 | test split < 10% (or any split < 5%) | MEDIUM |
| 5 | duplicate files in the manifest | MEDIUM |
| 6 | patient IDs that look like real names | HIGH |
| 7 | too few unique patients | LOW/MEDIUM |
Why this exists
A disturbing share of medical-AI papers report inflated accuracy because the
same patient ended up in both the training and test sets. Reviewers now check
for this. dicleak makes the check a one-liner.
Sample
samples/flawed_splits.csv is a deliberately broken manifest (leaked patients,
single-class val split, tiny test set, name-like IDs...). It scores 26/100:
dicleak samples/flawed_splits.csv
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for dicleak 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| dicleak-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.7 kB
Release files / dicleak-0.1.0.tar.gz
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| Size | 8.3 kB |
| Tags | Source |
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| Tags | Python 3 |
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