Summary of Investigating the Quality Of Dermamnist and Fitzpatrick17k Dermatological Image Datasets, by Kumar Abhishek et al.
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the quality of three popular dermatological image datasets: DermaMNIST, HAM10000, and Fitzpatrick17k. They find that these datasets contain issues such as duplicates, mislabeled images, and incorrect train-test partitions. The team analyzes the impact of these problems on benchmark results and proposes corrections to ensure the reproducibility of their findings. By making their analysis pipeline and code publicly available, they aim to encourage similar explorations and identify potential data quality issues in other datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers look at three important datasets for diagnosing skin conditions. They check these datasets and find some problems, like having the same images twice or not labeling pictures correctly. This can affect how well AI models work. The team shows what happens when you have these problems and suggests ways to fix them. By sharing their code and process, they want others to look at other big datasets for similar issues. |
