CleanPatrick: A Benchmark for Image Data Cleaning

• URL: http://arxiv.org/abs/2505.11034v1
• Date: Fri, 16 May 2025 09:29:41 GMT
• Title: CleanPatrick: A Benchmark for Image Data Cleaning
• Authors: Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Elisabeth Victoria Goessinger, Hanna Lindemann, Marie Bargiela, Marie Hofbauer, Omar Badri, Philipp Tschandl, Arash Koochek, Matthew Groh, Alexander A. Navarini, Marc Pouly,
• Abstract summary: CleanPatrick is the first large-scale benchmark for data cleaning in the image domain.<n>We collect 496,377 binary annotations from 933 medical crowd workers.<n>We employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth.
• Score: 31.45060372924389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance. We introduce CleanPatrick, the first large-scale benchmark for data cleaning in the image domain, built upon the publicly available Fitzpatrick17k dermatology dataset. We collect 496,377 binary annotations from 933 medical crowd workers, identify off-topic samples (4%), near-duplicates (21%), and label errors (22%), and employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth. CleanPatrick formalizes issue detection as a ranking task and adopts typical ranking metrics mirroring real audit workflows. Benchmarking classical anomaly detectors, perceptual hashing, SSIM, Confident Learning, NoiseRank, and SelfClean, we find that, on CleanPatrick, self-supervised representations excel at near-duplicate detection, classical methods achieve competitive off-topic detection under constrained review budgets, and label-error detection remains an open challenge for fine-grained medical classification. By releasing both the dataset and the evaluation framework, CleanPatrick enables a systematic comparison of image-cleaning strategies and paves the way for more reliable data-centric artificial intelligence.

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