{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-augmentation-for-skin-lesion-analysis","title":"Data Augmentation for Skin Lesion Analysis","arxiv_id":"1809.01442","date":"2018-09-05","proceeding":null,"authors":["Fábio Perez","Cristina Vasconcelos","Sandra Avila","Eduardo Valle"],"abstract":"Deep learning models show remarkable results in automated skin lesion\nanalysis. However, these models demand considerable amounts of data, while the\navailability of annotated skin lesion images is often limited. Data\naugmentation can expand the training dataset by transforming input images. In\nthis work, we investigate the impact of 13 data augmentation scenarios for\nmelanoma classification trained on three CNNs (Inception-v4, ResNet, and\nDenseNet). Scenarios include traditional color and geometric transforms, and\nmore unusual augmentations such as elastic transforms, random erasing and a\nnovel augmentation that mixes different lesions. We also explore the use of\ndata augmentation at test-time and the impact of data augmentation on various\ndataset sizes. Our results confirm the importance of data augmentation in both\ntraining and testing and show that it can lead to more performance gains than\nobtaining new images. The best scenario results in an AUC of 0.882 for melanoma\nclassification without using external data, outperforming the top-ranked\nsubmission (0.874) for the ISIC Challenge 2017, which was trained with\nadditional data.","url_abs":"http://arxiv.org/abs/1809.01442v1","url_pdf":"http://arxiv.org/pdf/1809.01442v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-augmentation-for-skin-lesion-analysis","repo_url":"https://github.com/fabioperez/skin-data-augmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"skin-cancer-classification","task_name":"Skin Cancer Classification"},{"task_slug":"skin-lesion-classification","task_name":"Skin Lesion Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"random-erasing","method_name":"Random Erasing"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01442"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/fabioperez/skin-data-augmentation","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"a6325d95120af3d0","entry":"random_combination","repo":"fabioperez/skin-data-augmentation","repo_kind":"official","path":"generate_lesions.py","file_url":"https://github.com/fabioperez/skin-data-augmentation/blob/HEAD/generate_lesions.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a6325d95120af3d0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}