{"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/recod-titans-at-isic-challenge-2017","title":"RECOD Titans at ISIC Challenge 2017","arxiv_id":"1703.04819","date":"2017-03-14","proceeding":null,"authors":["Afonso Menegola","Julia Tavares","Michel Fornaciali","Lin Tzy Li","Sandra Avila","Eduardo Valle"],"abstract":"This extended abstract describes the participation of RECOD Titans in parts 1\nand 3 of the ISIC Challenge 2017 \"Skin Lesion Analysis Towards Melanoma\nDetection\" (ISBI 2017). Although our team has a long experience with melanoma\nclassification, the ISIC Challenge 2017 was the very first time we worked on\nskin-lesion segmentation. For part 1 (segmentation), our final submission used\nfour of our models: two trained with all 2000 samples, without a validation\nsplit, for 250 and for 500 epochs respectively; and other two trained and\nvalidated with two different 1600/400 splits, for 220 epochs. Those four\nmodels, individually, achieved between 0.780 and 0.783 official validation\nscores. Our final submission averaged the output of those four models achieved\na score of 0.793. For part 3 (classification), the submitted test run as well\nas our last official validation run were the result from a meta-model that\nassembled seven base deep-learning models: three based on Inception-V4 trained\non our largest dataset; three based on Inception trained on our smallest\ndataset; and one based on ResNet-101 trained on our smaller dataset. The\nresults of those component models were stacked in a meta-learning layer based\non an SVM trained on the validation set of our largest dataset.","url_abs":"http://arxiv.org/abs/1703.04819v1","url_pdf":"http://arxiv.org/pdf/1703.04819v1.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":"recod-titans-at-isic-challenge-2017","repo_url":"https://github.com/learningtitans/isbi2017-part1","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"recod-titans-at-isic-challenge-2017","repo_url":"https://github.com/learningtitans/isbi2017-part3","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"recod-titans-at-isic-challenge-2017","repo_url":"https://github.com/Abdulrahman-Adel/Skin-Cancer-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recod-titans-at-isic-challenge-2017","repo_url":"https://github.com/learningtitans/data-depth-design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"skin-lesion-segmentation","task_name":"Skin Lesion Segmentation"}],"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":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"inception-a","method_name":"Inception-A"},{"method_slug":"inception-b","method_name":"Inception-B"},{"method_slug":"inception-c","method_name":"Inception-C"},{"method_slug":"inception-v4","method_name":"Inception-v4"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"reduction-a","method_name":"Reduction-A"},{"method_slug":"reduction-b","method_name":"Reduction-B"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}