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About

About

I'm Guilherme Aresta and I'm a PhD student/researcher at INESC TEC and Faculdade de Engenharia da Universidade do Porto (FEUP).

I've obtained my master degree in Bioengineering, field of Biomedical Engineering, at FEUP. 

My fields of interest are medical image analysis, computer vision and machine learning. My current research topic is the detection of lung nodules in computed tomography images.

Interest
Topics
Details

Details

002
Publications

2021

LNDb Challenge on automatic lung cancer patient management

Authors
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;

Publication
Medical Image Analysis

Abstract

2021

Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

Authors
Magalhaes, SA; Castro, L; Moreira, G; dos Santos, FN; Cunha, M; Dias, J; Moreira, AP;

Publication
Sensors

Abstract
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15, an mAP of 51.46 and an inference time of 16.44ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5ms.

2020

Automatic lung nodule detection combined with gaze information improves radiologists' screening performance

Authors
Aresta, G; Ferreira, C; Pedrosa, J; Araujo, T; Rebelo, J; Negrao, E; Morgado, M; Alves, F; Cunha, A; Ramos, I; Campilho, A;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract

2020

LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening

Authors
Pedrosa, J; Aresta, G; Rebelo, J; Negrao, E; Ramos, I; Cunha, A; Campilho, A;

Publication
IFMBE Proceedings

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening. © 2020, Springer Nature Switzerland AG.

2020

DR|GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images

Authors
Araujo, T; Aresta, G; Mendonca, L; Penas, S; Maia, C; Carneiro, A; Maria Mendonca, AM; Campilho, A;

Publication
Medical Image Analysis

Abstract