Agma Juci Machado Traina
CV Lattes
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
She has a Bachelor degree in Computer Science from the ICMSC-USP (1983), a Master degree in Computer Science and Computational Mathematics from the ICMSC-USP (1987), and a PhD in Computational Applied Physics from the IFQSC-USP (1991). She also spent a sabbatical leave at the Carnegie Mellon University (1998-2000) working on Multimedia Databases. She has been a Full Professor with the Department of Computer Science at ICMC-USP since 2008. She participated in and coordinated several national and international projects, integrating multidisciplinary teams of researchers and students. She has published more than 250 papers in selective journals and conferences, where she has received several awards for best paper, as well as mentored more than 50 graduate students. She participates in several editorial committees of journals and steering committees of conferences in her field. Her research interests include indexing and retrieval of complex data by content, similarity queries, data visualization, visual data mining, as well as image and video processing. Agma has been working in the integration of the results of her research lines with applications for the areas of medicine and agro-climatology, aiming at the development of applied computational systems. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| Big data analytics tools provide clinical decision support |
| Researchers develop smartphone app to support malaria diagnosis |
- Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD), AP.TEM
Abstract
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, im...
- 28th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2015), AO.R
- The 30th ACM/SIGAPP symposium on Applied Computing, AR.EXT
- 26th IEEE International Symposium on computer based Medical Systems (CBMs 2013), AR.EXT
(Only some records are available in English at this moment)
- Machine learning methods consolidation for MIVisBD, BP.TT
- Content-based retrieval of medical images to aid the clinical decision using radiomics, BP.DR
Abstract
Nowadays there is a huge amount of images daily produced. There is no difference in the medical scenario, in which a lot of images are produced in many exams and need to be stored and analyzed. However, the manual analysis and annotation is a time consuming task and requires a great knowledge for each image analyzed. To aid in this task, content-based image retrieval - CBIR systems are ...
- Detection of patterns and anomalies in medical data using Mathematical Modeling, BP.PD
Abstract
This postdoctoral research project aims at investigating the following question: How can we discover useful and needed patterns in large datasets of electronic health records and present this information to healthcare providers in an easy and succinct way? The hypothesis of this project is that mathematical models supported by data mining algorithms are effective tools to detect and to ...
(Only some records are available in English at this moment)
- Formalization and management of content-based information retrieval of complex objects with hierarchical and cross-modal data, BP.DR
Abstract
Image based reports, such as papers, web pages and thesis have been digitally stored, with fast growing in volume and complexity. This scenario challenges both database management systems' efficiency and effectiveness. Big data addresses issues found when dealing with great data volume, scope, complexity and distribution. On the other hand, as complexity grows, new query methods are nee...
- An approach to semantic segmentation exploiting multimodality and temporal characteristics of digital videos, BP.DR
Abstract
In a scenario of intense generation and dissemination of multimedia data, a growing demand for technology processing, compression and retrieval for these data, especially digital videos is observed. Accuracy and efficiency are critical factors to enable methods of storing and managing applications of large volumes of video data. Several Content- based Video Retrieval Systems - (CBVR) ha...
- Study and development of metric access methods using semantic data grouping, BP.DR
Abstract
The significant growth in both the amount and variety of data available for analysis has increased the need to develop methodologies for understanding, processing and summarizing data in a quick and automatic manner. One of the features that has proven critical for understanding and learning from large amounts of data is related to the data organization into groups by synthesis of what ...
(Only some records are available in English at this moment)
- Video event recognition with deep learning, BE.EP.DR
Abstract
The huge amount of videos made available online every day has created a demand for intelligent tools supporting basic multimedia systems needs as indexing, retrieval and automatic tagging. Robust event recognition strategy can directly aid such needs. A common approach for this task is to extract low-level features from the videos and to use classical machine learning techniques to mode...
- Boosting the applicability of metric access methods, BE.EP.DR
Abstract
In the last years, due to advances in technology, both the amount and variety of data available for analysis have significantly increased. This scenario has intensified the development of strategies, for fast and automatic processing of such data. Metric access methods (MAMs) are strategic for these purposes, because they aim at indexing the data, bringing effective ways for acceleratin...
- Content-based information retrieval in social media services, BE.EP.DR
Abstract
Social media services such as Facebook, Flickr and Google+ allow a single user to share multiple images, videos and comments. This generates a large volume of data, presenting a great challenging for information retrieval tasks. Unlike conventional data as numbers and short strings, for images and videos make no sense to compare them in their original domain. This kind of data is known ...
(Only some records are available in English at this moment)
| 1 / 1 | Ongoing research grants |
| 14 / 11 | Completed research grants |
| 4 / 4 | Ongoing scholarships in Brazil |
| 31 / 14 | Completed scholarships in Brazil |
| 5 / 4 | Completed scholarships abroad |
| 55 / 34 | All research grants and scholarships |
| Associated processes | |
Managing Complex Big Databases to Support Decision Making in Health Systems
Published in November 26, 2019 - FAPESP NewsAgency. Technical Session 5: Data Science & Machine Learning for Complex and Dynamical Models. Managing Complex Big Databases to Support Decision Making in Health Systems. One of the biggest challenges posed to the computer science field is integrating, organizing, and taking advantage of very large volumes of multi-modal data from diversified platforms for boosting decision-making processes. That is, how is it possible to use data from a variety of sources (patient’s exams, monitoring and treatment) to gather information from similar cases and build a better understanding of the case under analysis? Moreover, many times the data are not complete but cannot be discharged, as in the usual approach. In this talk, I will briefly discuss challenges and ongoing researches at the University of Sao Paulo that deal with such challenges in the context of health systems.(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 13 |
| Citations | 19 |
| Cit./Article | 1.5 |
| Data from Web of Science | |
SPADON, GABRIEL; BRANDOLI, BRUNO; ELER, DANILO M.; RODRIGUES-, JR., JOSE F.. Detecting multi-scale distance-based inconsistencies in cities through complex-networks. JOURNAL OF COMPUTATIONAL SCIENCE, v. 30, p. 209-222, JAN 2019. Web of Science Citations: 1. (16/17078-0, 17/08376-0, 16/02557-0)
CAZZOLATO, MIRELA T.; SCABORA, LUCAS C.; NESSO-JR, MARCOS R.; MILANO-OLIVEIRA, LUIS F.; COSTA, ALCEU F.; KASTER, DANIEL S.; KOENIGKAM-SANTOS, MARCEL; DE AZEVEDO-MARQUES, PAULO MAZZONCINI; TRAINA-JR, CAETANO; TRAINA, AGMA J. M.. dp-BREATH: Heat maps and probabilistic classification assisting the analysis of abnormal lung regions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 173, p. 27-34, MAY 2019. Web of Science Citations: 2. (18/24414-2, 16/17078-0, 16/17330-1)
FERREIRA-JUNIOR, JOSE RANIERY; KOENIGKAM-SANTOS, MARCEL; MAGALHAES TENORIO, ARIANE PRISCILLA; FALEIROS, MATHEUS CALIL; GARCIA CIPRIANO, FEDERICO ENRIQUE; FABRO, ALEXANDRE TODOROVIC; NAPPI, JANNE; YOSHIDA, HIROYUKI; DE AZEVEDO-MARQUES, PAULO MAZZONCINI. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, NOV 2019. Web of Science Citations: 0. (16/17078-0, 14/50889-7)
FELIPE, JOAQUIM CEZAR; TRAINA, JR., CAETANO; MACHADO TRAINA, AGMA JUCI. A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images. JOURNAL OF DIGITAL IMAGING, v. 22, n. 2, p. 183-201, APR 2009. Web of Science Citations: 8.
BUENO‚ R.; TRAINA‚ A.J.M.; TRAINA‚ C.. Genetic algorithms for approximate similarity queries. DATA & KNOWLEDGE ENGINEERING, v. 62, n. 3, p. 459-482, 2007.
BRINIS, SAFIA; TRAINA, JR., CAETANO; TRAINA, AGMA J. M.. Hollow-tree: a metric access method for data with missing values. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, v. 53, n. 3, SI, p. 481-508, DEC 2019. Web of Science Citations: 0. (16/17078-0)
GIUNTINI, FELIPE T.; CAZZOLATO, MIRELA T.; DOS REIS, MARIA DE JESUS DUTRA; CAMPBELL, ANDREW T.; TRAINA, AGMA J. M.; UEYAMA, JO. A review on recognizing depression in social networks: challenges and opportunities. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, JAN 2020. Web of Science Citations: 0. (16/17078-0, 18/24414-2, 13/07375-0, 18/17335-9)
DE SOUSA‚ E.P.M.; TRAINA‚ A.J.M.; TRAINA‚ C.; FALOUTSOS‚ C.. Measuring evolving data streams’ behavior through their intrinsic dimension. NEW GENERATION COMPUTING, v. 25, n. 1, p. 33-60, 2006. (05/04272-9)
BALAN, ANDRE G. R.; TRAINA, AGMA J. M.; RIBEIRO, MARCELA X.; MARQUES, PAULO M. A.; TRAINA, JR., CAETANO. Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. COMPUTERS IN BIOLOGY AND MEDICINE, v. 42, n. 5, p. 509-522, MAY 2012. Web of Science Citations: 3. (07/50285-0, 05/04272-9)
ARANTES, ADRIANO SIQUEIRA; VIEIRA, MARCOS R.; TRAINA JUNIOR, CAETANO; TRAINA, AGMA J. M.. Efficient algorithms to execute complex similarity queries in RDBMS. Journal of the Brazilian Computer Society, v. 9, n. 3, p. 5-24, Apr. 2004.
BLANCO, GUSTAVO; TRAINA, AGMA J. M.; TRAINA JR, CAETANO; AZEVEDO-MARQUES, PAULO M.; JORGE, ANA E. S.; DE OLIVEIRA, DANIEL; BEDO, MARCOS V. N.. A superpixel-driven deep learning approach for the analysis of dermatological wounds. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 183, JAN 2020. Web of Science Citations: 0. (16/17078-0)
OLIVEIRA, PAULO H.; SCABORA, LUCAS C.; CAZZOLATO, MIRELA T.; OLIVEIRA, WILLIAN D.; PAIXAO, RAFAEL S.; TRAINA, AGMA J. M.; TRAINA, CAETANO. Employing Domain Indexes to Efficiently Query Medical Data From Multiple Repositories. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v. 23, n. 6, p. 2220-2229, NOV 2019. Web of Science Citations: 1. (16/17078-0, 15/15392-7, 16/17330-1)
FELIPE, JOAQUIM CEZAR; TRAINA, JR., CAETANO; MACHADO TRAINA, AGMA JUCI. A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images. JOURNAL OF DIGITAL IMAGING, v. 22, n. 2, p. 183-201, APR 2009. Web of Science Citations: 8.
BUENO‚ R.; TRAINA‚ A.J.M.; TRAINA‚ C.. Genetic algorithms for approximate similarity queries. DATA & KNOWLEDGE ENGINEERING, v. 62, n. 3, p. 459-482, 2007.
CAZZOLATO, MIRELA T.; SCABORA, LUCAS C.; NESSO-JR, MARCOS R.; MILANO-OLIVEIRA, LUIS F.; COSTA, ALCEU F.; KASTER, DANIEL S.; KOENIGKAM-SANTOS, MARCEL; DE AZEVEDO-MARQUES, PAULO MAZZONCINI; TRAINA-JR, CAETANO; TRAINA, AGMA J. M.. dp-BREATH: Heat maps and probabilistic classification assisting the analysis of abnormal lung regions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 173, p. 27-34, MAY 2019. Web of Science Citations: 2. (18/24414-2, 16/17078-0, 16/17330-1)
FERREIRA-JUNIOR, JOSE RANIERY; KOENIGKAM-SANTOS, MARCEL; MAGALHAES TENORIO, ARIANE PRISCILLA; FALEIROS, MATHEUS CALIL; GARCIA CIPRIANO, FEDERICO ENRIQUE; FABRO, ALEXANDRE TODOROVIC; NAPPI, JANNE; YOSHIDA, HIROYUKI; DE AZEVEDO-MARQUES, PAULO MAZZONCINI. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, NOV 2019. Web of Science Citations: 0. (16/17078-0, 14/50889-7)
SPADON, GABRIEL; BRANDOLI, BRUNO; ELER, DANILO M.; RODRIGUES-, JR., JOSE F.. Detecting multi-scale distance-based inconsistencies in cities through complex-networks. JOURNAL OF COMPUTATIONAL SCIENCE, v. 30, p. 209-222, JAN 2019. Web of Science Citations: 1. (16/17078-0, 17/08376-0, 16/02557-0)
(References retrieved automatically from State of São Paulo Research Institutions)
COSTA, Alceu Ferraz. Mineração de Dados de Atividade de Usuários em Serviços de Mídia Social. 2017. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BUGATTI, Pedro Henrique. Arcabouço para recuperação de imagens por conteúdo visando à percepção do usuário. 2012. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
SILVA, Marcelo Ponciano da. Processamento de consultas por similaridade em imagens médicas visando à recuperação perceptual guiada pelo usuário. 2009. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
SILVA, Sergio Francisco da. Seleção de características por meio de algoritmos genéticos para aprimoramento de rankings e de modelos de classificação. 2011. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
COSTA, Alceu Ferraz. Mineração de imagens médicas utilizando características de forma. 2012. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
AVALHAIS, Letrícia Pereira Soares. Transformação de espaços métricos otimizando a recuperação de imagens por conteúdo e avaliação por análise visual. 2012. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
SOUZA, Jessica Andressa de. Agrupamento de dados complexos para apoiar consultas por similaridade com tratamento de restrições. 2018. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
COLTURATO, Danielle Bentivoglio. Ambiente interativo para visualização de dados de neurônios. 2001. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
PEDROSA, Glauco Vitor. Caracterização e recuperação de imagens usando dicionários visuais semanticamente enriquecidos. 2015. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CHINO, Daniel Yoshinobu Takada. Mineração de padrões frequentes em séries temporais para apoio à tomada de decisão em agrometereologia. 2014. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BUENO, Josiane Maria. Suporte à recuperação de imagens médicas baseadas em conteúdo através de Histogramas Métricos.. 2002. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
JÚNIOR, José Fernando Rodrigues. "Desenvolvimento de um Framework para Análise Visual de Informações Suportando Data Mining". 2003. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
CHINO, Fabio Jun Takada. "Visualizando a organização e o comportamento de estruturas métricas: aplicações em consultas por similaridade". 2004. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BALAN, André Guilherme Ribeiro. Métodos adaptativos de segmentação aplicados à recuperação de imagens por conteúdo. 2007. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
JÚNIOR, José Fernando Rodrigues. Design espacial-perceptivo: uma nova compreensão para representações visuais interativas. 2007. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
BUGATTI, Pedro Henrique. Análise da influência de funções de distância para o processamento de consultas por similaridade em recuperação de imagens por conteúdo. 2008. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.
RIBEIRO, Marcela Xavier. Suporte a sistemas de auxílio ao diagnóstico e de recuperação de imagens por conteúdo usando mineração de regras de associação. 2008. Tese (Doutorado) – Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos.