CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Diedre works with AI, computer vision and image processing since undergraduate research projects in 2013 to current PhD studies. Graduated in Computer Engineering from the Federal University of Bahia in 2017, including a sandwich year in Canada at the University of Alberta, with 3 months of research under Prof. Dr. Michael Bowling. After graduation, Diedre worked as a Computer Engineer at ATON Engenharia, applying Computer Vision knowledge to improve and deploy state-of-the-art algorithms into existing vision based smoke and fire detection systems. His master's degree (2018-2020) with a full time FAPESP scholarship was at UNICAMP, applying Deep Learning to hippocampus segmentation, under the guidance of professors Roberto Lotufo and Letícia Rittner. Currently working full time on his PhD, under the same supervision, using deep learning for automatic segmentation and quantification of structures and findings in CT images of the lung. (Source: Lattes Curriculum)
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The use of deep learning for medical decision support over Computed Tomography (CT) and X-Ray images in the current COVID pandemic is a hot topic in medical imaging processing. Research has shown that CT images display signs of COVID infection, which are more prevalent in severe cases. Some methods have already been proposed, mainly using transfer learning techniques over already proven m…
Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays, segmentation is still mainly performed manually by experts. Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and hard task, requirin…
The use of deep learning for automated segmentation of the lung and COVID-19 findings using computed tomography (CT) images in the current COVID-19 pandemic is a very active research topic in medical imaging segmentation. Some automated segmentation methods have already been proposed with supervised learning over manual annotations. The lack of availability of manual annotations data pair…
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