7 citations · 7 across the 3 of their papers we have counts for
4 papers
CleanPatrick: A Benchmark for Image Data Cleaning
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +12
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 releva…
Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm
Matthew Groh, Caleb Harris, Roxana Daneshjou +2
While artificial intelligence (AI) holds promise for supporting healthcare providers and improving the accuracy of medical diagnoses, a lack of transparency in the composition of d…
Can self-training identify suspicious ugly duckling lesions?
Mohammadreza Mohseni, Jordan Yap, William Yolland +2
One commonly used clinical approach towards detecting melanomas recognises the existence of Ugly Duckling nevi, or skin lesions which look different from the other lesions on the s…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…