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Adversarial Representation Learning for Medical Imaging
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| 40.99 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Breast cancer is a significant cause of death worldwide, especially among women, being one of the
hottest topics in the medical area. In 2020, according to the World Health Organization, there were 2 million women diagnosed with this disease and 685.000 deaths globally. Thus, demonstrating the enormous
impact that this disease has and hence the theme of this work being focused on breast cancer. Nowadays, most medical cases use CAD (Computer-Aided Diagnosis) systems in various ways to prevent and
help doctors attenuate the impact of cancer by combining their expertise and the advanced technology
we have today to perform various tasks. These learning-based systems use many high-quality datasets
to extract and identify core aspects and execute multiple tasks. However, there is significant difficulty
accessing these datasets because of data protection rules or even different data sharing policies, allied to
the nonexistence of suitable enough public datasets and labelled data. Regarding this problem and the
growing use of CADs systems in the breast cancer topic, this work proposes generating mammograms
based on a single mammogram allowing health entities to generate their mammograms and, thus, a highquality dataset. With that goal, this project uses the base work of ConSinGAN to generate images based
on a single one and an innovative way of gaining more image variability by using single image composition harmonisation. The results underwent a validation process, where the images’ quality, diversity and
impact were analysed. In terms of real-life usage, there is still a long way to go since such images need
to be validated by real doctors and generated at much higher resolutions. However, for now, it is already
a significant step toward this purpose.
Descrição
Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Teses de mestrado - 2023