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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
006
Publications

2024

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

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

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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

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.

Supervised
thesis

2023

Deep Learning Algorithms for Anatomical Landmark Detection

Author
Miguel Lopes Martins

Institution
UP-FCUP

2023

Echocardiography Automatic Image Quality Enhancement Using Generative Adversarial Networks

Author
Teresa Isabel da Silva Corado

Institution
UP-FCUP

2023

Multimodal deep learning for heart sound and electrocardiogram classification

Author
Hélder Miguel Carvalho Vieira

Institution
UP-FCUP

2023

Real time tracking of padel game movements, statistics and points

Author
João Pedro Andrade Ferreira

Institution
UP-FCUP

2023

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

Author
Maria Pedroso da Silva

Institution
UP-FCUP