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About

About

Prof. Miguel Coimbra is currently a Full Professor at the Computer Science Department of the Faculty of Sciences of the University of Porto. He is a member of the Executive Board of the Faculty of Sciences of the University of Porto since 2019, current coordinator of the TEC4Health line of INESC TEC, and past Chair of the Portugal Chapter of the IEEE Engineering and Medicine Society (2017-2021). He was one of the founders of IT Porto in 2007, its coordinator during 2015-19 and founder of the Interactive Media Group at this institute. He was the Director of the Master in Medical Informatics of the University of Porto between 2014-16, and was a co-founder in 2013 of IS4H - Interactive Systems for Healthcare, a spin-off company of the University of Porto. Prof. Coimbra leads and participates in various projects involving engineering and medicine, namely cardiology and gastroenterology, with current and past collaborations with hospitals in Portugal, Brazil (Pernambuco, Paraíba, Minas Gerais, São Paulo), Germany and Sweden. The nearly 16 years of experience in biomedical signal processing and interactive systems for healthcare have led to the development and deployment of systems for the collection and analysis of auscultation signals, echocardiography image processing for rheumatic fever screening, monitoring of stress and fatigue of firefighters in action, endoscopy signal analysis for cancer detection, computer assisted decision systems for capsule endoscopy, and quantification of 3d motion patterns for epilepsy, among others. Prof. Coimbra has more than 130 scientific publications, 25 of which in high-impact scientific journals (17 IEEE Transactions) and has attracted and managed more than 2M€ in research funding, over a total of 15 research projects acting as the PI of the project (10 projects) or co-PI of its Institution (5 projects).

Details

Details

  • Name

    Miguel Coimbra
  • Role

    TEC4 Coordinator
  • Since

    15th September 1998
  • Nationality

    Portugal
  • Contacts

    +351222094106
    miguel.coimbra@inesctec.pt
004
Publications

2023

Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram

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

Publication
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.

2023

The selection of an optimal segmentation region in physiological signals

Authors
Oliveira, J; Carvalho, M; Nogueira, D; Coimbra, M;

Publication
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

2023

Colonoscopic Polyp Detection with Deep Learning Assist

Authors
Neto, A; Couto, D; Coimbra, MT; Cunha, A;

Publication
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2023, Volume 4: VISAPP, Lisbon, Portugal, February 19-21, 2023.

Abstract
Colorectal cancer is the third most common cancer and the second cause of cancer-related deaths in the world. Colonoscopic surveillance is extremely important to find cancer precursors such as adenomas or serrated polyps. Identifying small or flat polyps can be challenging during colonoscopy and highly dependent on the colonoscopist's skills. Deep learning algorithms can enable improvement of polyp detection rate and consequently assist to reduce physician subjectiveness and operation errors. This study aims to compare YOLO object detection architecture with self-attention models. In this study, the Kvasir-SEG polyp dataset, composed of 1000 colonoscopy annotated still images, were used to train (700 images) and validate (300images) the performance of polyp detection algorithms. Well-defined architectures such as YOLOv4 and different YOLOv5 models were compared with more recent algorithms that rely on self-attention mechanisms, namely the DETR model, to understand which technique can be more helpful and reliable in clinical practice. In the end, the YOLOv5 proved to be the model achieving better results for polyp detection with 0.81 mAP, however, the DETR had 0.80 mAP proving to have the potential of reaching similar performances when compared to more well-established architectures. © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

2023

Assisted probe guidance in cardiac ultrasound: A review

Authors
Ferraz, S; Coimbra, M; Pedrosa, J;

Publication
FRONTIERS IN CARDIOVASCULAR MEDICINE

Abstract
Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

2023

Gastric cancer detection based on Colorectal Cancer transfer learning

Authors
Nobrega, S; Neto, A; Coimbra, M; Cunha, A;

Publication
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy.

Supervised
thesis

2022

Collaborative Tools for Lung Cancer Diagnosis in Computed Tomography

Author
Carlos Alexandre Nunes Ferreira

Institution
UP-FEUP

2021

Deep convolutional neural networks for gastric landmark detection

Author
Inês Filipa Fernandes Videira Lopes

Institution
UA-UA

2021

Heart Sound Analysis for Cardiovascular Diseases Identification

Author
Diogo Marcelo Esterlita Nogueira

Institution
UP-FCUP

2021

Underwater SLAM in featureless scenarios

Author
Bruno Lopes Matias

Institution
UP-FEUP

2021

Deteção de lesões pulmonares para rastreio de COVID-19

Author
Joana Soares Maximino

Institution
UP-FCUP