Deep learning and intermediate representations for pediatric image analysis

• Grant number: 20/06744-5
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2020
• End date: May 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Roberto Marcondes Cesar Junior
• Grantee: Hugo Neves de Oliveira
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery, AP.TEM

Abstract

Medical imaging requires the development of methods to improve the accuracy of the results of image analysis. Advances in medical image analysis provide such tools, but there is still an important gap in relation to pediatric brain imaging, although there is an increasing medical demand. This project aims to contribute to fill this gap, focusing on brain Magnetic Resonance (MR) of babies, newborns and premature babies, which raise specific questions due to the particular contrast of gray/white matter related to the physiological myelination process, to the evolution very fast, but not continuously observed, of brain structures and possible pathologies, as well as high intra and inter-subject variability. One of these issues is that the data is typically noisy, ambiguous, scarce and sparse over time. In turn, specialized medical knowledge is available, but it is prone to change and evolution. From this point of view, the project addresses one of the cutting edge issues in data analysis, that is, how to extract and understand significant patterns where data is scarce, but specialized knowledge, continuously enriched, is available. We propose to develop structural representations of knowledge and image information in the form of graphs and hypergraphs, which will be explored to guide the understanding of space-time images (segmentation, recognition, quantification, comparison over time, description of the image content and evolution). Such techniques will be complemented by deep learning approaches for processing 2D or 3D images. The objective of the project is to develop computational methods to support the diagnosis, pathology analysis and monitoring of patients. (AU)

Scientific publications

(11)

(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)

MONTEIRO, BRUNO A. A.; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.. Self-Supervised Learning for Seismic Image Segmentation From Few-Labeled Samples. IEEE Geoscience and Remote Sensing Letters, v. 19, p. 5-pg., 2022-01-01. (20/06744-5, 15/22308-2)

OLIVEIRA, HUGO; PENTEADO, LARISSA; MACIEL, JOSE LUIZ; FERRACIOLLI, SUELY FAZIO; TAKAHASHI, MARCELO STRAUS; BLOCH, ISABELLE; CESAR JUNIOR, ROBERTO; IEEE COMP SOC. Automatic Segmentation of Posterior Fossa Structures in Pediatric Brain MRIs. 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021), v. N/A, p. 8-pg., 2021-01-01. (19/16112-9, 15/22308-2, 18/07386-5, 20/06744-5, 17/50236-1)

NUNES, IAN M.; POGGI, MARCUS; OLIVEIRA, HUGO; PEREIRA, MATHEUS B.; DOS SANTOS, JEFERSSON A.; DECARVALHO, BM; GONCALVES, LMG. Deep Open-Set Segmentation in Visual Learning. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), v. N/A, p. 6-pg., 2022-01-01. (20/06744-5, 15/22308-2)

GAMA, PEDRO H. T.; OLIVEIRA, HUGO; MARCATO JR, JOSE; DOS SANTOS, JEFERSSON A.. Weakly Supervised Few-Shot Segmentation via Meta-Learning. IEEE TRANSACTIONS ON MULTIMEDIA, v. 25, p. 14-pg., 2023-01-01. (20/06744-5)

NUNES, IAN; PEREIRA, MATHEUS B.; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.; POGGI, MARCUS; IEEE. CONDITIONAL RECONSTRUCTION FOR OPEN-SET SEMANTIC SEGMENTATION. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, v. N/A, p. 5-pg., 2022-01-01. (20/06744-5, 15/22308-2)

OLIVEIRA, HUGO; CESAR, ROBERTO M., JR.; GAMA, PEDRO H. T.; DOS SANTOS, JEFERSSON A.; DECARVALHO, BM; GONCALVES, LMG. Domain Generalization in Medical Image Segmentation via Meta-Learners. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), v. N/A, p. 6-pg., 2022-01-01. (20/06744-5, 15/22308-2)

CARVALHO, THIAGO; MARTINEZ, JORGE A. CHAMORRO; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.; FEITOSA, RAUL QUEIROZ. Outlier Exposure for Open Set Crop Recognition From Multitemporal Image Sequences. IEEE Geoscience and Remote Sensing Letters, v. 20, p. 5-pg., 2023-01-01. (20/06744-5, 15/22308-2)

GAMA, PEDRO HENRIQUE TARGINO; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.; CESAR JR, ROBERTO M.. An overview on Meta-learning approaches for Few-shot Weakly-supervised Segmentation. COMPUTERS & GRAPHICS-UK, v. 113, p. 12-pg., 2023-05-28. (15/22308-2, 17/50236-1, 20/06744-5)

CHAMORRO MARTINEZ, JORGE A.; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.; FEITOSA, RAUL QUEIROZ. Open Set Semantic Segmentation for Multitemporal Crop Recognition. IEEE Geoscience and Remote Sensing Letters, v. 19, 2022. (17/50236-1, 20/06744-5, 15/22308-2)

NUNES, IAN; LARANJEIRA, CAMILA; OLIVEIRA, HUGO; DOS SANTOS, JEFERSSON A.. A systematic review on open-set segmentation. COMPUTERS & GRAPHICS-UK, v. 115, p. 13-pg., 2023-10-01. (15/22308-2, 17/50236-1, 20/06744-5)

BADAIN, RAFAEL; DAMINELI, DANIEL S. C.; PORTES, MARIA TERESA; FEIJO, JOSE; BURATTI, STEFANO; TORTORA, GIORGIA; DE OLIVEIRA, HUGO NEVES; CESAR, ROBERTO M., JR.. AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells. JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, v. N/A, n. 196, p. 15-pg., 2023-06-01. (21/05363-0, 20/06744-5, 19/23343-7, 19/26129-6, 15/22308-2)