Paulo Mazzoncini de Azevedo Marques is a full-time Associate Professor of Medical Physics and Biomedical Informatics with a tenure position at the Medical Imaging, Hematology and Oncology Department, 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 previously worked on medical imaging quality control, and since 1996, his research has focused on medical image processing. 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 Precision Medicine Assisted by Artificial Intelligence. (Source: Lattes Curriculum)
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| Las máquinas que todo lo ven |
| The machines that see everything |
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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 …
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…
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…
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)
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…
Lung cancer is the leading cause of cancer-related deaths in the world. Therapy decision of lung cancer must consider several factors besides tumor staging, like histological subtype and genetic mutations. Radiomics can aid diagnosis, prognosis, and therapy decision of lung cancer by associating quantitative features extracted from medical images with patient clinical information (staging…
(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…
| 2 / 2 | Ongoing research grants |
| 18 / 10 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 30 / 10 | Completed scholarships in Brazil |
| 2 / 1 | Completed scholarships abroad |
| 56 / 27 | All research grants and scholarships |
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