CleanPatrick: New Benchmark for Evaluating Image Data Cleaning Methods
Researchers introduced CleanPatrick, a large-scale benchmark dataset for evaluating image data cleaning methods, built from a dermatology dataset with nearly 500,000 crowdsourced annotations. The benchmark identifies common data quality issues including off-topic samples, near-duplicates, and label errors across medical images. The work addresses a gap in machine learning by providing standardized evaluation methods for data cleaning strategies that better reflect real-world audit workflows.
CleanPatrick is a new benchmark dataset designed to evaluate methods for cleaning image data in machine learning pipelines. Built on the publicly available Fitzpatrick17k dermatology dataset, it incorporates 496,377 binary annotations collected from 933 medical crowd workers to identify three categories of data quality issues: off-topic samples (4%), near-duplicates (21%), and label errors (32%). The researchers used an aggregation model based on item-response theory followed by expert review to establish high-quality ground truth labels. The benchmark formalizes data cleaning as a ranking task using standard metrics that align with real audit workflows. Testing of eight different cleaning approaches—ranging from classical anomaly detectors to modern self-supervised methods—revealed that self-supervised representations excel at near-duplicate detection, classical methods perform competitively for off-topic detection under budget constraints, and detecting implausible labels in fine-grained medical classification remains challenging.
What's missing
The study does not discuss potential limitations of relying on crowdsourced annotations from medical workers or how inter-annotator agreement was handled beyond the item-response theory aggregation model. Additionally, the generalizability of findings to non-medical image domains or datasets with different characteristics is not addressed.
What different sources said
- arXiv cs.LGCenter
CleanPatrick: A Benchmark for Image Data Cleaning
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