"Computer Vision and Machine Learning Challenges in Cancer Research"

No próximo dia 1 de junho, pelas 14h00 na sala S2 do DCC (FC6 1.40),  o Prof. Hélder Oliveira irá dar uma palestra intitulada "Computer Vision and Machine Learning Challenges in Cancer Research".


A palestra é organizada pelo DCC-FCUP e pelo INESCTEC e é aberta a todos os interessados.


Short Bio:

Hélder P. Oliveira (Member, IEEE) received the Ph.D. degree in electrical and computer engineering from the University of Porto, Portugal, in 2013. He is currently working as Senior Researcher at INESC TEC and is the Leader of the Visual Computing and Machine Intelligence Area, and member of the coordination council of the Centre for Telecommunications and Multimedia. He is also one of the coordinators of the Data Science Hub at INESC TEC. He is also working at the Computer Science Department of the Faculty of Sciences of the University of Porto as an Invited Assistant Professor. He co-authored 40+ international peer-reviewed papers, have one patent conceded (Europe, China, Japan) and 80+ works in international conferences. In total, these publications have attracted +900 citations, with an h-index of 18 according to Google Scholar. His research interests include Computer Vision and Machine Learning on biomedical fields.



"Computer Vision and Machine Learning Challenges in Cancer Research"






In modern healthcare, especially in Cancer, medical imaging plays an important role throughout the clinical process, from diagnosis and treatment planning to surgery and follow-up research. Patient triage in acute and chronic care, imaging-guided interventions, and treatment planning optimization are now part of standard clinical practice in several subspecialties. Medical images normally face one or more of the following challenges during acquisition: low resolution (in the spatial and spectral domains); high-level of noise; low contrast appearance; presence of geometric deformations; and the presence of imaging artefacts. Finer spatial sampling, which can be achieved with longer acquisition times, can avoid the drawbacks of low resolution and low contrast. However, this also increases the probability of geometric transformations, such as patient movement, resulting in blurred images. Imaging artefacts also lead to challenging problems in medical image analysis. Learning methodologies could be also a way to help physicians in their daily tasks, for example when dealing with multiple data sources, data sparsity or providing information about visual findings using, for example, explainable methods. Our main activities are focused on interdisciplinary research, computational models on health data from multiple sources, models integrating clinical domain knowledge and transparent, causal and accurate algorithms for improving clinical outcomes.

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