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About

João Teixeira was born in Porto, Portugal, in 1991. He received his M.Sc. in Electrical and Computers Engineering at the Faculty of Engineering of University of Porto, Portugal, in 2014. In 2014, he started his Ph.D. studies at Faculty of Sciences, University of Porto, Portugal, under the joint program in Informatics (MAP-i). He has been working since 2015 as a researcher at INESC TEC, an R&D institute affiliated to University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI), and the Breast Research Group. João Teixeira has a standing collaboration and has been consulting for a R&D department of the Faculty of Medicine (FMUP), the Center for Health Technology and Services Research (CINTESIS), since 2013.
His main research interests include computer vision, image processing, signal processing, with particular interest in medical applications (Breast cancer and Respiratory conditions) and m-health initiatives.

For further information please consult the CV:
http://www.degois.pt/visualizador/curriculum.jsp?key=3395739315417314 

Interest
Topics
Details

Details

003
Publications

2019

Quality assessment and feedback of Smart Device Microphone Spirometry executed by children

Authors
Almeida, R; Pinho, B; Jacome, C; Teixeira, JF; Amaral, R; Lopes, F; Jacinto, T; Guedes, R; Pereira, M; Goncalves, I; Fonseca, JA;

Publication
6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings

Abstract
Smart device microphone spirometry, based on the audio recording of forced expiratory maneuver (FEM), can be a simple, ubiquitous and easy tool for patients to self-monitor their asthma. Automatic validity assessment is crucial to guarantee that the global effort of the FEM fulfil the admissible minimum or if the maneuver needs to be repeated. In this work an automatic method to classify the sounds from FEM with respect to global effort was developed and evaluated using data from 54 children (5-10 years). The method proposed was able to correctly classify the microphone spirometry with respect to admissible minimum of effort with an accuracy of 86% (specificity 87% and sensitivity 86%). This method can be used to provide immediate feedback of the correct execution of the maneuver, improving the clinical value and utility of this self-monitoring tool.

2019

Lightweight deep learning pipeline for detection, segmentation and classification of breast cancer anomalies

Authors
Oliveira, HS; Teixeira, JF; Oliveira, HP;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation. © Springer Nature Switzerland AG 2019.

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Authors
Zolfagharnasab, H; Bessa, S; Oliveira, SP; Faria, P; Teixeira, JF; Cardoso, JS; Oliveira, HP;

Publication
Sensors

Abstract

2018

Three-dimensional planning tool for breast conserving surgery: A technological review

Authors
Oliveira, SP; Morgado, P; Gouveia, PF; Teixeira, JF; Bessa, S; Monteiro, JP; Zolfagharnasab, H; Reis, M; Silva, NL; Veiga, D; Cardoso, MJ; Oliveira, HP; Ferreira, MJ;

Publication
Critical Reviews in Biomedical Engineering

Abstract
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery. © 2018 by Begell House, Inc.

2018

Automatic Quality Assessment of Smart Device Microphone Spirometry

Authors
Pinho, B; Almeida, R; Jácome, C; Teixeira, JF; Amaral, R; Lopes, F; Jacinto, T; Guedes, R; Pereira, M; Gonçalves, I; Fonseca, J;

Publication
Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems

Abstract

Supervised
thesis

2017

Automated Detection of Bone structur keypoints on Magnetic Resonance Imaging - Sternum and Clavicules

Author
Beatriz Gonçalves Rocha

Institution
UP-FEUP

2017

Automated Detection of Anatomical structure keypoints on medical imaging algorithms directed for X-Ray Mammography

Author
Hugo Manuel Soares Oliveira

Institution
UP-FCUP

2015

Conceção e desenvolvimento de uma aplicação móvel para monitoramento da tosse em crianças

Author
José Manuel da Silva Fernandes

Institution
UP-FMUP