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Presentation

Biomedical Engineering Research

At C-BER our main goals are the creation of interdisciplinary knowledge enabling innovation and technology transfer with economic impact; and also the development of products, tools and methods for the prevention and early detection of different types of diseases, aging-related impairments, or for human rehabilitation, physical therapy or functional assessment.

We also seek to contribute to the development of advanced neuro-technologies at the frontier of engineering and neurology, and to promote strategic partnerships with clinical partners, research institutes, and fostering international cooperation.

Our R&D activity is developed in three different areas: BioInstrumentation, Biomedical Imaging and NeuroEngineering.

Latest News
Networked Intelligent Systems

INESC TEC Spin-off awarded €100K to expand clinical trials in patients with Parkinson's disease

The spin-off inSignals Neurotech, dedicated to the development of medical devices for the quantification of motor symptoms of neurodegenerative diseases, in order to obtain better clinical results, received €100K through the INNOV-ID funding call - an initiative promoted by Portugal Ventures, focusing on the measures established by the Government to capitalise Portuguese companies during the pandemic.

26th March 2021

Networked Intelligent Systems

Unprecedented study sugests improvements in the diagnosis and monitoring in patients with familial amyloid polyneuropathy

In the scientific article “Clinical 3-D Gait Assessment of Patients With Polyneuropathy Associated With Hereditary Transthyretin Amyloidosis”, published in Frontiers in Neurology, one of the most important journals in the area, the researchers analysed the gait parameters of different groups of patients with familial amyloid polyneuropathy, according to different stages of development.

18th December 2020

Networked Intelligent Systems

INESC TEC developed a tool for the automatic analysis of X-rays images, in order to assess the evolution of COVID-19 patients

A team of researchers from INESC TEC’s Centre for Biomedical Engineering Research (C-BER) developed a computer-assisted diagnosis system, in partnership with radiologists from the Vila Nova de Gaia/Espinho hospital centre (CHVNGE) and the Northern Region Health Administration (ARS Norte).

14th December 2020

Networked Intelligent Systems

INESC TEC technology in the first neurostimulator in the world using long-term monitoring in epileptic patient

The new neurostimulator produced by the North-American company Medtronic relies on technology developed by INESC TEC’s Centre for Biomedical Engineering Research (C-BER), in order to measure the movements caused by epileptic events in 3D, and connect them with deep brain stimulation, as well as brain surface signals (EEG). This device was approved for human use in Europe in January this year, and it enables stimulating and capturing brain signals simultaneously, which can make all the difference in the patients’ quality of life. For the first time in the world, this neurostimulator was implanted in an epileptic patient, and used in a study on 3D EEG video technology in late July, at the University Hospital Centre of São João (CHUSJ).

14th September 2020

Networked Intelligent Systems

INESC TEC spin-off acknowledged as one of the most promising in the world in digital health

ILoF – Intelligent on Fiber, a spin-off from the University of Porto, established in INESC TEC and currently incubated at the Faculty of Medicine (FMUP), is among the 150 most promising start-ups in the field of digital health worldwide, according to a list provided by CB Insights, a North American company specialised in business analysis.

31st August 2020

Interest Topics
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Featured Projects

iHandUApp

iHandU App development

2021-2022

Bio_Support

Serviços de consultoria especializada no desenvolvimento estratégico de sistemas biomédicos e licenciamento de software

2021-2023

AgWearCare

Wearables para Monitorização das Condições de Trabalho no Agroflorestal

2021-2023

iHandU_v2

New iHandU prototype development & small-serie (20) hardware production

2021-2021

CAGED

Computer Assisted Gastric Cancer Diagnosis

2021-2024

THOR

THOR - Computer Assisted Thoracic Assessment using POCUS

2021-2024

C4MiR_BIP_Proof

C4MiR_BIP_Proof_PROGRAMA DE ATRIBUIÇÃO DE APOIOS PARA PROVA DE CONCEITO DA UNIVERSIDADE DO PORTO

2020-2021

iiLab

Ampliação da Infraestrutura Tecnológica do INESC TEC para a Transformação Digital da Indústria

2020-2022

VitalPROVID

VitalPROVID

2020-2021

CAIRUS

COVID-19 Artificial Intelligence-based Risk Unified Stratification tool for clinical management

2020-2020

CXR_AI4COVID19

Chest Radiography-based AI for Supporting ClinicalDecision on Covid-19

2020-2020

TAMI

Transparent Artificial Medical Intelligence

2020-2023

BioNanoTech

Serviços de apoio técnico - iLoF spin-off

2020-2020

MRI_Simulator

Aluguer do Simulador de Ressonância Magnética à Faculdade de Psicologia e de Ciências da Educação da Universidade do Porto

2019-2019

WalkingPAD

Patient education on a quantified supervised home-based exercise therapy to improve walking ability in patients with peripheral arterial disease and intermittent claudication

2019-2022

Serv_Neuro

Consultoria Insignals Neurotech no âmbito do programa EIT Health Startups Meets Pharma 2019

2019-2019

LUCAS

Lung cancer screening - A non-invasive methodology for early diagnosis

2018-2022

PERFECT

Perceptual equivalence in virtual reality for authentic training

2018-2021

TexBoost

Less Commodities more Specialities

2017-2020

LNDetector

Automatic Detection, Segmentation and Classification of Pulmonary Nodules System in Computed Tomography Images

2016-2019

SCREEN-DR

Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening

2016-2020

Bio-Early

Projeto Vital Sticker no âmbito do Contrato Programa

2015-2018

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

VR2Market

VR2Market: Towards a Mobile Wearable Health Surveillance Product for First Response and other Hazardous Professions

2014-2019

STePMotion

Spatio-temporal components of the processing of sensorial and motor information

2014-2015

EcoDrive

Inteligent Eco Driving and Fleet Management

2014-2015

Re-Learning

Human motor re-learning by sensor information fusion

2014-2015

VitalResponder2

Intelligent management of critical events of stress, fatigue and smoke intoxication in forest firefighting

2013-2015

ASD-MD

Movement Disorders in Autistic Spectrum Disorders

2013-2015

HERMES

Interactive system for digital content consumers

2013-2015

Team
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Laboratories

Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab

Publications

C-BER Publications

View all Publications

2021

Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment

Authors
Zhang, O; Ding, C; Pereira, T; Xiao, R; Gadhoumi, K; Meisel, K; Lee, RJ; Chen, YR; Hu, X;

Publication
IEEE Access

Abstract

2021

Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics

Authors
Pinto, JR; Correia, MV; Cardoso, JS;

Publication
IEEE Transactions on Biometrics, Behavior, and Identity Science

Abstract

2021

Efficient reactive obstacle avoidance using spirals for escape

Authors
Azevedo, F; Cardoso, JS; Ferreira, A; Fernandes, T; Moreira, M; Campos, L;

Publication
Drones

Abstract
The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

2021

LNDb Challenge on automatic lung cancer patient management

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Atwal, G; Phoulady, HA; Chen, XY; Chen, RZ; Li, JL; Wang, LS; Galdran, A; Bouchachia, H; Kaluva, KC; Vaidhya, K; Chunduru, A; Tarai, S; Nadimpalli, SPP; Vaidya, S; Kim, I; Rassadin, A; Tian, ZH; Sun, ZW; Jia, YZ; Men, XJ; Ramos, I; Cunha, A; Campilho, A;

Publication
Medical Image Analysis

Abstract

2021

Epistemic and Heteroscedastic Uncertainty Estimation in Retinal Blood Vessel Segmentation

Authors
Costa, P; Smailagic, A; Cardoso, JS; Campilho, A;

Publication
U.Porto Journal of Engineering

Abstract
Current state-of-the-art medical image segmentation methods require high quality datasets to obtain good performance. However, medical specialists often disagree on diagnosis, hence, datasets contain contradictory annotations. This, in turn, leads to difficulties in the optimization process of Deep Learning models and hinder performance. We propose a method to estimate uncertainty in Convolutional Neural Network (CNN) segmentation models, that makes the training of CNNs more robust to contradictory annotations. In this work, we model two types of uncertainty, heteroscedastic and epistemic, without adding any additional supervisory signal other than the ground-truth segmentation mask. As expected, the uncertainty is higher closer to vessel boundaries, and on top of thinner and less visible vessels where it is more likely for medical specialists to disagree. Therefore, our method is more suitable to learn from datasets created with heterogeneous annotators. We show that there is a correlation between the uncertainty estimated by our method and the disagreement in the segmentation provided by two different medical specialists. Furthermore, by explicitly modeling the uncertainty, the Intersection over Union of the segmentation network improves 5.7 percentage points.

Facts & Figures

7Senior Researchers

2016

1R&D Employees

2020

6Academic Staff

2020

Contacts