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Detalhes

Detalhes

  • Nome

    Aurélio Campilho
  • Cargo

    Investigador Afiliado
  • Desde

    01 janeiro 2014
  • Nacionalidade

    Portugal
  • Contactos

    +351222094106
    aurelio.campilho@inesctec.pt
006
Publicações

2022

Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection

Autores
Pedrosa, J; Sousa, P; Silva, J; Mendonça, AM; Campilho, A;

Publicação
Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, Proceedings

Abstract

2022

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning

Autores
Pedrosa, J; Aresta, G; Ferreira, C; Carvalho, C; Silva, J; Sousa, P; Ribeiro, L; Mendonca, AM; Campilho, A;

Publicação
SCIENTIFIC REPORTS

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients. © 2022, The Author(s).

2022

Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans

Autores
Meiburger, KM; Marzola, F; Zahnd, G; Faita, F; Loizou, CP; Laine, N; Carvalho, C; Steinman, DA; Gibello, L; Bruno, RM; Clarenbach, R; Francesconi, M; Nicolaides, AN; Liebgott, H; Campilho, A; Ghotbi, R; Kyriacou, E; Navab, N; Griffin, M; Panayiotou, AG; Gherardini, R; Varetto, G; Bianchini, E; Pattichis, CS; Ghiadoni, L; Rouco, J; Orkisz, M; Molinari, F;

Publicação
COMPUTERS IN BIOLOGY AND MEDICINE

Abstract
After publishing an in-depth study that analyzed the ability of computerized methods to assist or replace human experts in obtaining carotid intima-media thickness (CIMT) measurements leading to correct therapeutic decisions, here the same consortium joined to present technical outlooks on computerized CIMT measurement systems and provide considerations for the community regarding the development and comparison of these methods, including considerations to encourage the standardization of computerized CIMT measurements and results presentation. A multi-center database of 500 images was collected, upon which three manual segmentations and seven computerized methods were employed to measure the CIMT, including traditional methods based on dynamic programming, deformable models, the first order absolute moment, anisotropic Gaussian derivative filters and deep learning-based image processing approaches based on U-Net convolutional neural networks. An inter- and intra-analyst variability analysis was conducted and segmentation results were analyzed by dividing the database based on carotid morphology, image signal-to-noise ratio, and research center. The computerized methods obtained CIMT absolute bias results that were comparable with studies in literature and they generally were similar and often better than the observed inter- and intra-analyst variability. Several computerized methods showed promising segmentation results, including one deep learning method (CIMT absolute bias = 106 +/- 89 mu m vs. 160 +/- 140 mu m intra-analyst variability) and three other traditional image processing methods (CIMT absolute bias = 139 +/- 119 mu m, 143 +/- 118 mu m and 139 +/- 136 mu m). The entire database used has been made publicly available for the community to facilitate future studies and to encourage an open comparison and technical analysis

2021

LNDb Challenge on automatic lung cancer patient management

Autores
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;

Publicação
MEDICAL IMAGE ANALYSIS

Abstract

2021

Epistemic and Heteroscedastic Uncertainty Estimation in Retinal Blood Vessel Segmentation

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

Publicação
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.

Teses
supervisionadas

2021

Explainable Artificial Medical Intelligence for Automated Thoracic Pathology Screening

Autor
Joana Maria Neves da Rocha

Instituição
UP-FEUP

2021

Artificial Intelligence-based Decision Support Models for COVID-19 Detection

Autor
Sofia Perestrelo de Vasconcelos Cardoso Pereira

Instituição
UP-FCUP

2021

Multi-Objective Long-Term Transmission Expansion Planning

Autor
Luiz Eduardo de Oliveira

Instituição
UP-FEUP

2021

Detection of lung nodules in computed tomography images

Autor
Guilherme Moreira Aresta

Instituição
UP-FEUP

2020

Detection of lung nodules in computed tomography images

Autor
Guilherme Moreira Aresta

Instituição
UP-FEUP