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

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Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018., , , , , and . CoRR, (2018)Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation., , and . CoRR, (2019)Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation., , and . CVPR Workshops, page 3182-3191. Computer Vision Foundation / IEEE, (2020)Bibliometric Analysis of Information Systems Related to Innovation., , , , and . ITQM, volume 55 of Procedia Computer Science, page 298-307. Elsevier, (2015)Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches., , , , , and . CoRR, (2023)Automatic segmentation of vocal tract articulators in real-time magnetic resonance imaging., , , , , and . Comput. Methods Programs Biomed., (January 2024)Automatic generation of the complete vocal tract shape from the sequence of phonemes to be articulated., , , , and . Speech Commun., (2022)Autoencoder-Based Tongue Shape Estimation During Continuous Speech., and . INTERSPEECH, page 86-90. ISCA, (2022)Towards the Prediction of the Vocal Tract Shape from the Sequence of Phonemes to be Articulated., , , , and . Interspeech, page 3325-3329. ISCA, (2021)Recommending comprehensive solutions for programming tasks by mining crowd knowledge., , , , , and . ICPC, page 358-368. IEEE / ACM, (2019)