# Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018

arXiv: https://arxiv.org/abs/1808.08480 · HTML: https://mlsanity.com/paper/1808.08480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2203.14341/) (2022)
- [Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine](https://mlsanity.com/paper/2411.19447/) (2024)
- [Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation](https://mlsanity.com/paper/2605.09600/) (2026)

## Abstract (arXiv)

This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018). Although our team has a long experience with melanoma classification and moderate experience with lesion segmentation, the ISIC Challenge 2018 was the very first time we worked on lesion attribute detection. For each task we submitted 3 different ensemble approaches, varying combinations of models and datasets. Our best results on the official testing set, regarding the official metric of each task, were: 0.728 (segmentation), 0.344 (attribute detection) and 0.803 (classification). Those submissions reached, respectively, the 56th, 14th and 9th places.