DermSynth3D: Synthesis of in-the-wild annotated dermatology images-Reference-Cited by-同舟云学术

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DermSynth3D: Synthesis of in-the-wild annotated dermatology images

Published:2024-07 Issue: Volume:95 Page:103145
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Sinha Ashish^ORCID,Kawahara Jeremy^ORCID,Pakzad Arezou^ORCID,Abhishek Kumar^ORCID,Ruthven Matthieu^ORCID,Ghorbel Enjie^ORCID,Kacem Anis^ORCID,Aouada Djamila^ORCID,Hamarneh Ghassan^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds National de la Recherche Luxembourg

BC Cancer Foundation

Nvidia

Alliance de recherche numérique du Canada

Publisher

Elsevier BV

Reference99 articles.

1. Mask2Lesion: Mask-constrained adversarial skin lesion image synthesis;Abhishek,2019

2. Abhishek, K., Hamarneh, G., Drew, M.S., 2020. Illumination-Based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 728–729.

3. Predicting the clinical management of skin lesions using deep learning;Abhishek;Sci. Rep.,2021

4. Scape: shape completion and animation of people;Anguelov,2005

5. Scape: shape completion and animation of people;Anguelov,2005

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1. Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

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