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About

About

Joana Rocha started her Integrated Master's in Bioengineering at the University of Porto in 2014, focusing on computer vision and artificial intelligence for biomedical applications. She joined Swansea University to study human motion patterns, developing an automated measurement technique for physical activity assessment. In 2018, she joined INESC-TEC, where she worked on a computer-aided diagnosis system for lung cancer, biometrics for presentation attack detection, and is now working on explainable AI for automated thoracic pathology screening.

Interest
Topics
Details

Details

  • Name

    Joana Maria Rocha
  • Role

    Research Assistant
  • Since

    18th June 2019
  • Nationality

    Portugal
  • Contacts

    +351222094000
    joana.m.rocha@inesctec.pt
001
Publications

2021

Segmentation of COVID-19 Lesions in CT Images

Authors
Rocha, J; Pereira, S; Campilho, A; Mendonça, AM;

Publication
2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Abstract

2021

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Authors
Rocha J.; Mendonça A.M.; Campilho A.;

Publication
U.Porto Journal of Engineering

Abstract
Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

2021

Chest Radiography Few-Shot Image Synthesis for Automated Pathology Screening Applications

Authors
Sousa, MQE; Pedrosa, J; Rocha, J; Pereira, SC; Mendonça, AM; Campilho, A;

Publication
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Abstract

2020

Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images

Authors
Rocha, J; Cunha, A; Mendonca, AM;

Publication
Journal of Medical Systems

Abstract

2020

Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter

Authors
Rocha, J; Cunha, A; Mendonca, AM;

Publication
IFMBE Proceedings

Abstract
This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization. © 2020, Springer Nature Switzerland AG.