Generating unlimited skin lesion images with generative adversarial networks

• Grant number: 19/19619-7
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: June 01, 2020
• End date: February 29, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Sandra Eliza Fontes de Avila
• Grantee: Alceu Emanuel Bissoto
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID
• Associated scholarship(s): 22/09606-8 - Understanding the role of shortcuts and distribution shifts in deep learning generalization, BE.EP.DR

Abstract

Melanoma is the deadliest form of Skin Cancer. Automated skin lesion analysis can play an important role for early detection, which is crucial for patient's treatment. Deep neural networks are the current state-of-the-art for skin lesion analysis, but the lack of annotated data limits classification performance. We started in 2017 to generate annotated data for our classification models with Generative Adversarial Networks (GANs). A GAN can learn the data distribution, allowing us to sample from it, enabling a complementary method for data augmentation. In our previous work, we were able to generate high-resolution, clinically-meaningful skin lesion images that when used to augment a classification network's training data boosted performance. However, our method had limitations. The amount of synthetics we can generate is limited by a special annotation that is available only for a small subset of our training data. This limitation is troublesome because ideally we want to generate infinite amounts of data to aid skin lesion classification. In this Ph.D. proposal, we explore methods for infinite generation: we propose to incorporate relevant clinical information into the GAN generation process, maintaining quality without killing variability and quantity. We believe our experience in generative models and skin lesion analysis qualify this work, enabling us to improve the current state-of-the-art for skin lesion generation and classification. We highlight that our group is at the forefront of such research worldwide being responsible for groundbreaking results associated with skin lesion analysis. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BISSOTO, ALCEU; VALLE, EDUARDO; AVILA, SANDRA; IEEE COMP SOC. GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021), v. N/A, p. 10-pg., 2021-01-01. (19/19619-7, 13/08293-7)

MIRIKHARAJI, ZAHRA; ABHISHEK, KUMAR; BISSOTO, ALCEU; BARATA, CATARINA; AVILA, SANDRA; VALLE, EDUARDO; CELEBI, M. EMRE; HAMARNEH, GHASSAN. A survey on deep learning for skin lesion segmentation. MEDICAL IMAGE ANALYSIS, v. 88, p. 40-pg., 2023-06-19. (19/19619-7, 13/08293-7)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

BISSOTO, Alceu Emanuel. Avaliação e mitigação de vieses em análise de lesões de pele. 2024. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Instituto de Computação Campinas, SP.