Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
Bibliographic record
Abstract
Abstract The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. In this paper, we conduct meticulous analyses of three popular dermatological image datasets: DermaMNIST, its source HAM10000, and Fitzpatrick17k, uncovering these data quality issues, measure the effects of these problems on the benchmark results, and propose corrections to the datasets. Besides ensuring the reproducibility of our analysis, by making our analysis pipeline and the accompanying code publicly available, we aim to encourage similar explorations and to facilitate the identification and addressing of potential data quality issues in other large datasets. Citation If you find this project useful or if you use our newly proposed datasets and/or our analyses, please cite our paper. Kumar Abhishek, Aditi Jain, Ghassan Hamarneh. "Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets". arXiv preprint arXiv:2401.14497, 2024. DOI: 10.48550/ARXIV.2401.14497. The corresponding BibTeX entry is: @article{abhishek2024investigating, title={Investigating the Quality of {DermaMNIST} and {Fitzpatrick17k} Dermatological Image Datasets}, author={Abhishek, Kumar and Jain, Aditi and Hamarneh, Ghassan}, journal={arXiv preprint arXiv:2401.14497}, doi = {10.48550/ARXIV.2401.14497}, url = {https://arxiv.org/abs/2401.14497}, year={2024}} Project Website The results of the analysis, including the visualizations, are available on the project website: https://derm.cs.sfu.ca/critique/. Code The accompanying code for this project is hosted on GitHub at https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets. License The metadata files (DermaMNIST-C.csv, DermaMNIST-E.csv, Fitzpatrick17k_DiagnosisMapping.xlsx,Fitzpatrick17k-C.csv) contained in this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. The NPZ files associated with DermaMNIST-C (dermamnist_corrected_28.npz, dermamnist_corrected_224.npz) and DermaMNIST-E (dermamnist_extended_28.npz, dermamnist_extended_224.npz) contained in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. The code hosted on GitHub is licensed under the Apache License 2.0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".