António Cunha

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Prof. Auxiliar

Universidade de Trás-os-Montes e Alto Douro

Portugal

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Prof. António Cunha is an Auxiliar Professor at Universidade de Trás-os-Montes e Alto Douro, Portugal. His research areas encompass artificial intelligence, machine learning, and medical applications, focusing on optimizing active learning strategies and advancing explainable AI for medical classification tasks. Recent publications include studies on pneumonia detection, retinal disease classification, and efficient architectures for pathology detection.

Recent Grants

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CAGE: Computer Assisted Gastric Cancer Diagnosis

Open Date: 2021-03-01

Close Date: 2024-03-01

António Cunha

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Recent Grants

Articles (30)

PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays

CTCovid19: Automatic Covid-19 model for Computed Tomography Scans Using Deep Learning

Optimising Active Learning with a Decreasing-Budget-Based Strategy: A Medical Application Case Study

A Controlled Variation Approach for Example-Based Explainable AI in Colorectal Polyp Classification

Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review

Retinitis Pigmentosa Classification with Deep Learning and Integrated Gradients Analysis

Evaluating EfficientNet Architectures for Pathology Detection in Endoscopic Gastrointestinal Tract Images

A Multi-Stage Automatic Method Based on a Combination of Fully Convolutional Networks for Cardiac Segmentation in Short-Axis MRI

A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks-Using YOLO v8 and Dataset Review

Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review

Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Enhancing Image Annotation With Object Tracking and Image Retrieval: A Systematic Review

Enhancing Image Annotation With Object Tracking and Image Retrieval: A Systematic Review

Multiple Instance Learning in Medical Images: A Systematic Review

Multiple Instance Learning in Medical Images: A Systematic Review

Systematic review on weapon detection in surveillance footage through deep learning

Advancing Grapevine Variety Identification: A Systematic Review of Deep Learning and Machine Learning Approaches

Comparative Analysis of CNNs and Vision Transformers for Automatic Classification of Abandonment in Douro’s Vineyard Parcels

Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification

A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

A Systematic Review on Automatic Insect Detection Using Deep Learning

Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice

Detection and Mosaicing Techniques for Low-Quality Retinal Videos

Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

A Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Tracking

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

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

Collaborators (10)

Paulo Jorge Coelho

Joao Rodrigues

Joaquim João Sousa

Miguel Coimbra

Tânia Pereira

João Beirão

Mário Dinis-Ribeiro

António Jorge Gonçalves de Gouveia

Sandra Cunha

Helder Oliveira

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