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Sobre

Sobre

Miguel Coimbra, licenciado em Engenharia Eletrotécnica e de Computadores (Faculdade de Engenharia da Universidade do Porto) e doutorado em Engenharia Electrónica (Queen Mary, University of London), é Professor Catedrático no Departamento de Ciência de Computadores da Faculdade de Ciências da Universidade do Porto. É vogal do Conselho Executivo da Faculdade de Ciências da Universidade do Porto desde abril de 2019, coordenador da linha TEC4Health do INESC TEC desde janeiro de 2019, e coordenador do laboratório BioImaging Lab do INESC TEC desde janeiro de 2022. Foi presidente do Portugal Chapter da IEEE Engineering in Medicine and Biology Society entre 2018 e 2022. Foi um dos fundadores em 2007 da Delegação do Porto do Instituto de Telecomunicações, da qual foi coordenador entre 2015 e 2019. Nesta criou e coordenou entre 2008 e 2014 o grupo de Interactive Multimedia. Foi diretor entre 2014 e 2016 do Mestrado em Informática Médica da Universidade do Porto, e co-fundador em 2013 da IS4H – Interactive Systems for Healthcare, uma empresa spin-off da Universidade do Porto, onde licencia e vende produtos baseados nas tecnologias interativas de auscultação desenvolvidas pela sua equipa.

A nível de atividade científica liderou ou participou em múltiplos projetos na interface entre a ciência de computadores e a saúde, nomeadamente em cardiologia, gastroenterologia e reumatologia, com colaborações atuais e passadas com instituições de saúde em Portugal, Brasil (Pernambuco, Paraíba, Minas Gerais, São Paulo), Alemanha e Suécia. Os quase 15 anos de experiência em ciência de computadores, mais concretamente na área da informática para a saúde (visão computacional, processamento de sinal biomédico, interação pessoa-máquina), levaram ao desenvolvimento e instalação de sistemas para a colheita e análise de sinais de auscultação, processamento de imagens de ecocardiografia para rastreio de febre reumática, monitorização de stress e fadiga de bombeiros em ação, análise de imagem endoscópica para deteção de cancro, sistemas de apoio à decisão para cápsula endoscópica, e quantificação de padrões de movimento 3D para epilepsia, entre outros. É (co)-autor de um total de 133 publicações científicas, incluindo 3 capítulos em livros e 29 artigos em revista, sendo 25 destes em revistas de primeiro quartil, 17 dos quais nas prestigiadas IEEE Transactions. A nível de formação avançada já terminou com sucesso a orientação de 4 investigadores de pós-doutoramento, 6 estudantes de doutoramento e 47 estudantes de mestrado. Durante os últimos 13 anos atraiu e geriu mais de 2M€ de financiamento para investigação, distribuídos por um total de 16 projetos nacionais ou internacionais onde atuou como investigador principal do projeto ou como líder da equipa de investigação da sua instituição.


Detalhes

Detalhes

  • Nome

    Miguel Coimbra
  • Cargo

    Coordenador de TEC4
  • Desde

    15 setembro 1998
  • Nacionalidade

    Portugal
  • Contactos

    +351222094106
    miguel.coimbra@inesctec.pt
006
Publicações

2024

Separation of the Aortic and Pulmonary Components of the Second Heart Sound via Alternating Optimization

Autores
Renna, F; Gaudio, A; Mattos, S; Plumbley, MD; Coimbra, MT;

Publicação
IEEE ACCESS

Abstract
An algorithm for blind source separation (BSS) of the second heart sound (S2) into aortic and pulmonary components is proposed. It recovers aortic (A2) and pulmonary (P2) waveforms, as well as their relative delays, by solving an alternating optimization problem on the set of S2 sounds, without the use of auxiliary ECG or respiration phase measurement data. This unsupervised and data-driven approach assumes that the A2 and P2 components maintain the same waveform across heartbeats and that the relative delay between onset of the components varies according to respiration phase. The proposed approach is applied to synthetic heart sounds and to real-world heart sounds from 43 patients. It improves over two state-of-the-art BSS approaches by 10% normalized root mean-squared error in the reconstruction of aortic and pulmonary components using synthetic heart sounds, demonstrates robustness to noise, and recovery of splitting delays. The detection of pulmonary hypertension (PH) in a Brazilian population is demonstrated by training a classifier on three scalar features from the recovered A2 and P2 waveforms, and this yields an auROC of 0.76.

2024

Towards automatic forecasting of lung nodule diameter with tabular data and CT imaging

Autores
Ferreira, CA; Venkadesh, KV; Jacobs, C; Coimbra, M; Campilho, A;

Publicação
Biomed. Signal Process. Control.

Abstract
Objective: This study aims to forecast the progression of lung cancer by estimating the future diameter of lung nodules. Methods: This approach uses as input the tabular data, axial images from tomography scans, and both data types, employing a ResNet50 model for image feature extraction and direct analysis of patient information for tabular data. The data are processed through a neural network before prediction. In the training phase, class weights are assigned based on the rarity of different types of nodules within the dataset, in alignment with nodule management guidelines. Results: Tabular data alone yielded the most accurate results, with a mean absolute deviation of 0.99 mm. For malignant nodules, the best performance, marked by a deviation of 2.82 mm, was achieved using tabular data applying Lung-RADS class weights during training. The tabular data results highlight the influence of using the initial nodule size as an input feature. These results surpass the literature reference of 348-day volume doubling time for malignant nodules. Conclusion: The developed predictive model is optimized for integration into a clinical workflow after detecting, segmenting, and classifying nodules. It provides accurate growth forecasts, establishing a more objective basis for determining follow-up intervals. Significance: With lung cancer's low survival rates, the capacity for precise nodule growth prediction represents a significant breakthrough. This methodology promises to revolutionize patient care and management, enhancing the chances for early risk assessment and effective intervention. © 2024 The Author(s)

2024

A Comparative Study of Feature-Based and End-to-End Approaches for Lung Nodule Classification in CT Volumes to Lung-RADS Follow-up Recommendation

Autores
Ferreira, A; Ramos, I; Coimbra, M; Campilho, A;

Publicação
2024 IEEE 22nd Mediterranean Electrotechnical Conference, MELECON 2024

Abstract
Lung cancer represents a significant health concern necessitating diligent monitoring of individuals at risk. While the detection of pulmonary nodules warrants clinical attention, not all cases require immediate surgical intervention, often calling for a strategic approach to follow-up decisions. The LungRADS guideline serves as a cornerstone in clinical practice, furnishing structured recommendations based on various nodule characteristics, including size, calcification, and texture, outlined within established reference tables. However, the reliance on labor-intensive manual measurements underscores the potential advantages of integrating decision support systems into this process. Herein, we propose a feature-based methodology aimed at enhancing clinical decision-making by automating the assessment of nodules in computed tomography scans. Leveraging algorithms tailored for nodule calcification, texture analysis, and segmentation, our approach facilitates the automated classification of follow-up recommendations aligned with Lung-RADS criteria. Comparison with a previously reported end-to-end image-based classification method revealed competitive performance, with the feature-based approach achieving an accuracy of 0.701 ± 0.026, while the end-to-end method attained 0.727 ± 0.020. The inherent explainability of the feature-based approach offers distinct advantages, allowing clinicians to scrutinize and modify individual features to address disagreements or rectify inaccuracies, thereby tailoring follow-up recommendations to patient profiles. © 2024 IEEE.

2024

Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation

Autores
Kerdegari, H; Higgins, K; Veselkov, D; Laponogov, I; Polaka, I; Coimbra, M; Pescino, JA; Leja, M; Dinis-Ribeiro, M; Kanonnikoff, TF; Veselkov, K;

Publicação
DIAGNOSTICS

Abstract
The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes.

2023

Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram

Autores
Elola, A; Aramendi, E; Oliveira, J; Renna, F; Coimbra, MT; Reyna, MA; Sameni, R; Clifford, GD; Rad, AB;

Publicação
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Abstract
Objective: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. Methods: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. Results: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. Conclusions: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. Significance: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.

Teses
supervisionadas

2023

Multimodal deep learning for heart sound and electrocardiogram classification

Autor
Hélder Miguel Carvalho Vieira

Instituição
UP-FCUP

2023

Real time tracking of padel game movements, statistics and points

Autor
João Pedro Andrade Ferreira

Instituição
UP-FCUP

2023

Novel deep learning methods for characterization of precancerous tissue in endoscopic narrow band images

Autor
Maria Pedroso da Silva

Instituição
UP-FCUP

2023

Collaborative Tools for Lung Cancer Diagnosis in Computed Tomography

Autor
Carlos Alexandre Nunes Ferreira

Instituição
UP-FCUP

2023

Heart Sound Analysis for Cardiovascular Diseases Identification

Autor
Diogo Marcelo Esterlita Nogueira

Instituição
UP-FCUP