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About

Ricardo Pereira de Magalhães Cruz received a B.S. degree in computer science and an M.S. degree in applied mathematics, both from the University of Porto, Portugal. Since 2015, he has been a researcher at INESC TEC working in machine learning with a particular emphasis on computer vision. He is finishing his Ph.D. in computer science, in the MAP-i program, a joint program between the universities of Minho, Aveiro, and Porto.

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Publications

2021

Ordinal losses for classification of cervical cancer risk

Authors
Albuquerque, T; Cruz, R; Cardoso, JS;

Publication
PEERJ COMPUTER SCIENCE

Abstract
Cervical cancer is the fourth leading cause of cancer-related deaths in women, especially in low to middle-income countries. Despite the outburst of recent scientific advances, there is no totally effective treatment, especially when diagnosed in an advanced stage. Screening tests, such as cytology or colposcopy, have been responsible for a substantial decrease in cervical cancer deaths. Cervical cancer automatic screening via Pap smear is a highly valuable cell imaging-based detection tool, where cells must be classified as being within one of a multitude of ordinal classes, ranging from abnormal to normal. Current approaches to ordinal inference for neural networks are found to not sufficiently take advantage of the ordinal problem or to be too uncompromising. A non-parametric ordinal loss for neuronal networks is proposed that promotes the output probabilities to follow a unimodal distribution. This is done by imposing a set of different constraints over all pairs of consecutive labels which allows for a more flexible decision boundary relative to approaches from the literature. Our proposed loss is contrasted against other methods from the literature by using a plethora of deep architectures. A first conclusion is the benefit of using non-parametric ordinal losses against parametric losses in cervical cancer risk prediction. Additionally, the proposed loss is found to be the top-performer in several cases. The best performing model scores an accuracy of 75.6% for seven classes and 81.3% for four classes.

2019

Automatic Augmentation by Hill Climbing

Authors
Cruz, R; Pinto Costa, JFP; Cardoso, JS;

Publication
Lecture Notes in Computer Science - Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

Abstract

2019

Insulator visual non-conformity detection in overhead power distribution lines using deep learning

Authors
Prates, RM; Cruz, R; Marotta, AP; Ramos, RP; Simas Filho, EF; Cardoso, JS;

Publication
COMPUTERS & ELECTRICAL ENGINEERING

Abstract
Overhead Power Distribution Lines (OPDLs) correspond to a large percentage of the medium-voltage electrical systems. In these networks, visual inspection activities are usually performed without resorting to automated systems, requiring a significant investment of time and human resources. We present a methodology to identify the defect and type of insulators using Convolutional Neural Networks (CNNs). More than 2500 photographs were collected both from inside a studio and from a realistic OPDL. A classification model is proposed to automatically recognize the insulators conformity. This model is able to learn from indoors photographs by augmenting these images with realistic details such as top ties and real-world backgrounds. Furthermore, Multi-Task Learning (MTL) was used to improve performance of defect detection by also predicting the insulator class. The proposed methodology is able to achieve an accuracy of 92% for material classification and 85% for defect detection, with F1-score of 0.75, surpassing available solutions.

2019

Averse Deep Semantic Segmentation

Authors
Cruz, R; Costa, JFP; Cardoso, JS;

Publication
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Abstract

2018

A Class Imbalance Ordinal Method for Alzheimer's Disease Classification

Authors
Cruz, R; Silveira, M; Cardoso, JS;

Publication
2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

Abstract
The majority of computer-Aided diagnosis methods for Alzheimer's disease (AD) from brain images either address only two stages of the disease at a time (and reduce the problem to binary classification) or do not exploit the ordinal nature of the different classes. An exception is the work by Fan et al. [1], which proposed an ordinal method that obtained better performance than traditional multiclass classification. Still, special care should be taken when data is class imbalanced, i.e. when some classes are overly represented when compared to others. Building on top of [1], this work makes use of a recently published ordinal classifier, which transforms the problem into sets of pairwise ranking problems, in order to address the class imbalance in the data [2]. Several methods were experimented with, using a Support Vector Machine as the underlying estimator. The pairwise ranking approach has shown promising results, both for traditional and imbalance metrics. © 2018 IEEE.