We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| pbevan1/detecting-melanoma-fairly | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_mean_std | Ran | pbevan1/detecting-melanoma-fairly/preprocessing.py pointer only (licence: NONE) · get_code("ba8470699bf72aa4") |
| get_size_ISIC | Ran | pbevan1/detecting-melanoma-fairly/preprocessing.py pointer only (licence: NONE) · get_code("1e48325cf21a28e8") |
| get_size_from_raw | Ran | pbevan1/detecting-melanoma-fairly/preprocessing.py pointer only (licence: NONE) · get_code("e6d83a666e60e46d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use these automated labels as the target for two leading bias unlearning techniques towards mitigating skin tone bias. Our experimental results provide evidence that our skin tone detection algorithm outperforms existing solutions and that unlearning skin tone may improve generalisation and can reduce the performance disparity between melanoma detection in lighter and darker skin tones.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.02832")
get_code_for_paper("2202.02832")
have("2202.02832")
Connect an agent — have() is free.