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About

About

I’m an Assistant Professor at the University of Trás-os-Montes and Alto Douro (UTAD), Portugal since 1996 and I teach  Networks and Security. I graduated in 1993 and started working at STCP, the Public Transport's operator of Porto. I finish my master's thesis in 1998, and obtained my doctorate in 2005, in the area of computer vision related to control of automated guided vehicles.  I’m a member of Centre for Biomedical Engineering Research (C-BER), in the research center INESC TEC since 2014. My investigation is in Electrical Engineering, Electronics & Computers, with a particular focus in machine learning and biomedical image processing.

Interest
Topics
Details

Details

  • Name

    António Cunha
  • Role

    Senior Researcher
  • Since

    01st January 2014
  • Nationality

    Portugal
  • Contacts

    +351222094106
    antonio.cunha@inesctec.pt
004
Publications

2022

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Authors
Camara, J; Neto, A; Pires, IM; Villasana, MV; Zdravevski, E; Cunha, A;

Publication
JOURNAL OF IMAGING

Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

2022

Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

Authors
Sousa, J; Pereira, T; Silva, F; Silva, MC; Vilares, AT; Cunha, A; Oliveira, HP;

Publication
APPLIED SCIENCES-BASEL

Abstract
Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 ± 0.2140 and 0.8750 ± 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.

2022

Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device

Authors
Neto, A; Camara, J; Cunha, A;

Publication
SENSORS

Abstract
Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses.

2022

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Authors
Silva, F; Pereira, T; Neves, I; Morgado, J; Freitas, C; Malafaia, M; Sousa, J; Fonseca, J; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Costa, JL; Hespanhol, V; Cunha, A; Oliveira, HP;

Publication
JOURNAL OF PERSONALIZED MEDICINE

Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

2022

A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management

Authors
Esengönül, M; Marta, A; Beirão, J; Pires, IM; Cunha, A;

Publication
Medicina (Lithuania)

Abstract
Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.

Supervised
thesis

2021

A step closer to real-time detect gastric cancer

Author
Sara Pires da Nóbrega

Institution

2021

Ensemble methods for lung cancer gene mutation prediction

Author
Alexandra Ventura

Institution

2021

Prostate Cancer Automatic Grading from Digitized H&E-stained Histopathology Slides

Author
João Carlos Parada Alves

Institution
UP-FEUP

2021

Revisão de técnicas de pesquisa inspiradas em enxames

Author
Daniel da Silva Duarte

Institution
UTAD

2021

Automatic glaucoma screening with low cost devices

Author
Alexandre Henrique da Costa Neto

Institution