Computational Imaging Research

• Clinical Research
• Machine Learning Research
  • Hrvoje Bogunovic
  • Teresa Finisterra Araújo
  • Guilherme Moirera Aresta
  • Arunava Chakravarty
  • Taha Emre
  • Meltem Esengönül
  • Botond Fazekas
  • Hana Jebril
  • Dmitrii Lachinov
  • Marzieh Oghbaie
  • Antoine Rivail
  • José Morano Sánchez
  • Emese Sükei
  • Markus Unterdechler
• Software Development
• Administrative
• Reading Team
• Former Team Members

Guilherme Moreira Aresta, PhD

Postdoctoral Researcher
Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Email: guilherme.moreiraaresta(at)meduniwien.ac.at
Phone: +43 1 40400 66142
Office:  BT72, 17. floor, room 72.17.12

Bio:

Guilherme Aresta received his M.Sc. degree in Bioengineering – Biomedical engineering from the Faculty of Engineering of University of Porto, Porto, Portugal in 2016. He later pursued his Ph.D. degree in Electrical and Computer Engineering at the same faculty, having focused on the development of deep learning approaches for medical image analysis and especially lung cancer screening. He collaborated as a researcher with the Biomedical Imaging Laboratory, Center for Biomedical Engineering Research (C-BER) of the Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC) since 2014. Since 2021 he has joined the Christian Doppler Laboratory for Artificial Intelligence in Retina as a postdoc researcher.

Research Interests:
His research interests are related to the study of machine learning, deep learning and computer vision techniques for medical image analysis and computer-aided diagnosis.

Selected Publications:
Aresta, G., Araújo, T., Kwok, S.,et al.BACH: Grand challenge on breast cancer histologyimages. Medical Image Analysis, 56:122–139, 2019c. doi: 10.1016/j.media.2019.05.010(first two authors contributed equally)

Aresta, G., Jacobs, C., Araújo, T.,et al. iW-Net: an automatic and minimalistic interactivelung nodule segmentation deep network.Scientific Reports, 9(1):1–9, 2019e. doi: 10.1038/s41598-019-48004-8

Aresta, G., Ferreira, C., Pedrosa, J.,et al.Automatic Lung Nodule Detection CombinedWith Gaze Information Improves Radiologists’ Screening Performance. IEEE journal ofbiomedical and health informatics, 24(10):2894–2901, 2020. doi: 10.1109/JBHI.2020.2976150