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About

About

I obtained my integrated master's degree in Biomedical Engineering in 2018 at the University of Minho. The last two years of my degree were focused on medical informatics. Then, I joined HASLab in 2018, alongside the development of my master thesis named "Cloud-based Analytics for Monitoring and Classification of Arrhythmia".  Currently, I am a PhD student at HASLab/INESC TEC and I am enrolled in the doctoral program in Informatics (PDInf) in University of Minho. My main research interests fall into distributed and privacy-preserving machine learning. 

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Details

Details

  • Name

    Cláudia Vanessa Brito
  • Role

    Research Assistant
  • Since

    01st October 2018
003
Publications

2023

Generative Adversarial Networks in Healthcare: A Case Study on MRI Image Generation

Authors
Cepa, B; Brito, C; Sousa, A;

Publication
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Medical imaging, mainly Magnetic Resonance Imaging (MRI), plays a predominant role in healthcare diagnosis. Nevertheless, the diagnostic process is prone to errors and is conditioned by available medical data, which might be insufficient. A novel solution is resorting to image generation algorithms to address these challenges. Thus, this paper presents a Deep Learning model based on a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. Our model generates 2D MRI images of size 256x256, containing an axial view of the brain with a tumor. The model was implemented using ChainerMN, a scalable and flexible framework that enables faster and parallel training of Deep Learning networks. The images obtained provide an overall representation of the brain structure and the tumoral area and show considerable brain-tumor separation. For this purpose, and owing to their previous state-of-the-art results in general image-generation tasks, we conclude that GAN-based models are a promising approach for medical imaging.

2023

SOTERIA: Preserving Privacy in Distributed Machine Learning

Authors
Brito, C; Ferreira, P; Portela, B; Oliveira, R; Paulo, J;

Publication
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023

Abstract

2023

Promoting sustainable and personalised travel behaviours while preserving data privacy

Authors
Pina, N; Brito, C; Vitorino, R; Cunha, I;

Publication
Transportation Research Procedia

Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addrebes these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

2022

A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Authors
Brito, C; Esteves, M; Peixoto, H; Abelha, A; Machado, J;

Publication
WIRELESS NETWORKS

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

2022

Cloud-Based Privacy-Preserving Medical Imaging System Using Machine Learning Tools

Authors
Alves, J; Soares, B; Brito, C; Sousa, A;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
Healthcare environments are generating a deluge of sensitive data. Nonetheless, dealing with large amounts of data is an expensive task, and current solutions resort to the cloud environment. Additionally, the intersection of the cloud environment and healthcare data opens new challenges regarding data privacy. With this in mind, we propose MEDCLOUDCARE (MCC), a healthcare application offering medical image viewing and processing tools while integrating cloud computing and AI. Moreover, MCC provides security and privacy features, scalability and high availability. The system is intended for two user groups: health professionals and researchers. The former can remotely view, process and share medical imaging information in the DICOM format. Also, it can use pre-trained Machine Learning (ML) models to aid the analysis of medical images. The latter can remotely add, share, and deploy ML models to perform inference on DICOM images. MCC incorporates a DICOM web viewer enabling users to view and process DICOM studies, which they can also upload and store. Regarding the security and privacy of the data, all sensitive information is encrypted at rest and in transit. Furthermore, MCC is intended for cloud environments. Thus, the system is deployed using Kubernetes, increasing the efficiency, availability and scalability of the ML inference process.