CV Lattes
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Universidade de São Paulo (USP). Faculdade de Medicina de Ribeirão Preto (FMRP) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Paulo Mazzoncini de Azevedo Marques is a full-time Associate Professor of Medical Physics and Biomedical Informatics with a tenure position at the Department of Medical Imaging, Hematology and Oncology, University of Sao Paulo (USP), Ribeirao Preto Medical School (FMRP) Brazil. He received his B.Sc. and M.Sc. degrees in Electrical Engineering in 1986 and 1990, respectively, and his Ph.D. in Applied Physics in 1994 at the University of Sao Paulo. He had a research associate position at the University of Chicago in 2001, where he worked on medical image processing for computer-aided diagnosis (CAD) and content-based image retrieval (CBIR) under the supervision of Professor Kunio Doi. His main area of activity is Artificial Intelligence applied to healthcare. (Source: Lattes Curriculum)
| Articles published in Pesquisa FAPESP Magazine about the researcher: |
| Las máquinas que todo lo ven |
| The machines that see everything |
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Health systems form a complex structure whose understanding and modeling involve multiple factors and their relationships. Brazil's demographic diversity makes Health Management an even more challenging scenario. On the other hand, overcoming them can produce significant advances for Health in Brazil and worldwide. Advanced Artificial Intelligence (AI) methods have generated significant d…
Cardiovascular disease is the leading cause of death worldwide. Underlying atherosclerosis and ensuing conditions such as myocardial infarction, ischemic heart disease and stroke cause tremendous morbidity, mortality, and economic loss. Early identification of patients with high risk for such clinical events enables preventive actions. The use of machine learning (ML) for risk prediction …
The main goal of this proposal is to transfer for the Health System innovative medical applications oriented to computer-aided diagnosis, treatment, surgical planning, training and medical supportive decisions. The National Institute of Science and Technology in Medicine Assisted by Scientific Computing (INCT-MACC) has since 2008 successfully integrated a knowledge network for modern comm…
A recent and important advance in the quantitative analysis of images is the concept of Radiomics. Radiomics is an emerging field of radiology that is based on the conversion of image data into a characteristic space of high dimension, using different characteristic extraction algorithms. The rationale for radiomics is that a broader characterization of the underlying tumor phenotypes can…
Today, almost every human activity generates and/or demands to store and process massive, diverse and complex data, either in scientific, academic, enterprise and even leisure pursuits - the scenario being called as the “big data era”. Health-related activities are in the core of those activities, as they produce big data as well as they can take advantage of the technological storm, impr…
This project involves the application of knowledge from the field of information science to medicine and aims the construction and practical application of domain ontology for the medical specialty of Radiology and Diagnostic Imaging. Based on the intensive usage of information from several different types of examinations, and comprehensive coverage of terminology in the area, with the co…
(Only some records are available in English at this moment)
Although several machine learning (ML) models using electronic health record (EHR) data have been developed recently, there is a significant need for external validation of these models. Predictive models may perform well in a center with a population similar to the training data but may perform worse in centers with different patient characteristics. Multicenter studies should be conside…
Today the complexity of the unstructured nature of large datasets is known as the term Big Data and presents several challenges in tasks involving the prediction of new data. Specifically, Big Data in medical image classification presents the challenge that deals with the existence of a high number of unlabeled images compared to a reduced number of images labeled by specialists in the pr…
While several machine learning (ML) models that use electronic health record (EHR) data have been developed in recent years, there is a great need for external validation of these models. Prediction models may perform well in a center with a population similar to the training data, but may perform worse in centers with different patient characteristics. To develop generalizable models and…
Exploring large medical data repositories with distance-based query criteria is troublesome whenever the objects are too close to each other.For instance, previous studies suggest recovering too similar images may spoil the semantics of Content-based Medical Image Retrieval (CBMIR) applications as well as their usability in a clinical workflow. The main semantic drawback is too close imag…
(Only some records are available in English at this moment)
Medical image registration plays an important role in determining topographic and morphological changes for functional diagnostic and therapeutic purposes. Manual alignment and semi-automated softwares still have been used; however they are subjective and make specialists spend precious time. Fully automated methods are faster and user-independent, but the critical point is registration r…
| 3 / 3 | Ongoing grants |
| 19 / 11 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 32 / 12 | Completed scholarships in Brazil |
| 2 / 1 | Completed scholarships abroad |
| 59 / 30 | All research grants and scholarships |
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