INESC TEC
INESC TEC
INESC TEC
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Aurélio Joaquim de Castro Campilho

Aurélio Joaquim de Castro Campilho

Aurélio Campilho is Emeritus Professor of the University of Porto, Jubilee Full Professor in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Portugal. He is a Fellow from EAMBES (European Alliance of Medical and Biological Engineering and Science). He is a Senior Member of the IEEE – The Institute of Electrical and Electronics Engineers. He is coordinator of the Center for Biomedical Engineering Research (C-BER) and develops research at the Biomedical Imaging Lab from C-BER from INESC TEC – Institute for Systems and Computer Engineering, Technology and Science. His current research interests include the areas of biomedical engineering, medical image analysis, image processing and computer vision, particularly in Computer-aided Diagnosis applied in several imaging modalities, including ophthalmic images, carotid ultrasound imaging and computed tomography of the lung.

He is the author of one book (with two editions), co-edited 20 books and published more than 250 articles in international journals and conferences. Organized several special issues of magazines and conferences. He was Associate Editor of the journals IEEE Transactions on Biomedical Engineering and the Machine Vision Applications Journal. From 2004 to 2020, he was General Chair of the International Conferences on Image Analysis and Recognition (ICIAR) conference series.


 

 

Projects

NanoStima-RL5

The goal of CAD-RL5 is to develop advanced capabilities for computer-aided detection and diagnosis (CAD). This requires research on innovative methodologies for CAD development, that will make it possible to go from ad hoc engineering approaches, driven by direct expert knowledge, to more automated approaches, driven by the intrinsic structure of data, knowledge discovery and expert supervision. Problems tackled will be generic in the sense that appropriate outcomes can be applied universally to medical imaging practices. The developed method will enable lab demonstrations of several clinical problems where the research team has relevant experience (e.g. radiology, ophthalmology and ultrasound imaging). Project Information Sheet (PT)

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

TAMI

The aim of the project TAMI is to create a new platform for commercial, scientific and academic use that will provide "consumers" access to results and explanations of registered diagnostic orders, filtered data sets access for investigators or scientists and a knowledge base for academic purposes. In order to achieve such objective, the project will be based on the following specific objectives involving the development of research in the following areas: a) quantitative methods to objectively assess and compare different explanations of the automatic decisions; b) methods to generate better explanations, providing variety in the explanations, adapting the explanations to who will consume them and explaining multimodal decisions; c) novel visualization solutions for interpretations of decisions based on imagiological data. In order to accomplish that, TAMI will use clinical data, from structured to image data, in order to design and validate interpretable machine learning models. During the project, different multimodal settings will be tested to enable a better understanding of the AI-based decisions. Moreover, the algorithms will be designed to generate self-explanatory AIbased decisions, minimise bias, and act ethically in their context. Proof-of-concepts and demonstrators of how to integrate the researched explainable AI into workflows of cervical cancer treatment, pathology detection in chest X-Ray images in a screening environment, and glaucoma detection in retinal fundus images will be developed to validate the algorithmic solutions.

TAMI
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Publications

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

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

2024

Biomed. Signal Process. Control.

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

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

2024

2024 IEEE 22nd Mediterranean Electrotechnical Conference, MELECON 2024

Automated image label extraction from radiology reports - A review

Pereira, SC;Mendonca, AM;Campilho, A;Sousa, P;Lopes, CT;

2024

ARTIFICIAL INTELLIGENCE IN MEDICINE

Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions

Polonia, A;Campelos, S;Ribeiro, A;Aymore, I;Pinto, D;Biskup Fruzynska, M;Veiga, RS;Canas Marques, R;Aresta, G;Araujo, T;Campilho, A;Kwok, S;Aguiar, P;Eloy, C;

2021

AMERICAN JOURNAL OF CLINICAL PATHOLOGY

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Supervised theses

Quantitative imaging of oligodendrocyte cytoskeleton dynamics

Óscar Gonçalo Martins Esteves

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