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Sobre

Sobre

Pedro Neto é atualmente mestre em Computer Science pela Aalto University na Finlândia e estudante de doutoramento na FEUP. Em paralelo, trabalha como investigador bolseiro no INESC TEC no projecto CADPath, para o desenvolvimento de um sistema de apoio ao diagnostico do cancro colorectal e cervical, no Centro de Telecomunicações e Multimédia (CTM). Para além do projeto, Pedro produz trabalhos na área de Biometria, por exemplo reconhecimento facial, detecção de ataques de spoofing e na junção destes tópicos com a interpretabilidade dos modelos de inteligência artificial.

Tópicos
de interesse
Detalhes

Detalhes

002
Publicações

2023

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

Autores
Montezuma, D; Oliveira, SP; Neto, PC; Oliveira, D; Monteiro, A; Cardoso, JS; Macedo-Pinto, I;

Publicação
MODERN PATHOLOGY

Abstract
Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.(c) 2022 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

2023

A CAD system for automatic dysplasia grading on H&E cervical whole-slide images

Autores
Oliveira, SP; Montezuma, D; Moreira, A; Oliveira, D; Neto, PC; Monteiro, A; Monteiro, J; Ribeiro, L; Goncalves, S; Pinto, IM; Cardoso, JS;

Publicação
SCIENTIFIC REPORTS

Abstract
Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request.

2023

Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection

Autores
Caldeira, E; Neto, PC; Gonçalves, T; Damer, N; Sequeira, AF; Cardoso, JS;

Publicação
CoRR

Abstract

2023

A CAD System for Colorectal Cancer from WSI: A Clinically Validated Interpretable ML-based Prototype

Autores
Neto, PC; Montezuma, D; de Oliveira, SP; Oliveira, D; Fraga, J; Monteiro, A; Monteiro, JC; Ribeiro, L; Gonçalves, S; Reinhard, S; Zlobec, I; Pinto, IM; Cardoso, JS;

Publicação
CoRR

Abstract

2022

Myope Models - Are face presentation attack detection models short-sighted?

Autores
Neto, PC; Sequeira, AF; Cardoso, JS;

Publicação
2022 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WORKSHOPS (WACVW 2022)

Abstract
Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems. Humans often use background information as contextual cues for their visual system. Yet, regarding face-based systems, the background is often discarded, since face presentation attack detection (PAD) models are mostly trained with face crops. This work presents a comparative study of face PAD models (including multi-task learning, adversarial training and dynamic frame selection) in two settings: with and without crops. The results show that the performance is consistently better when the background is present in the images. The proposed multi-task methodology beats the state-of-the-art results on the ROSE-Youtu dataset by a large margin with an equal error rate of 0.2%. Furthermore, we analyze the models' predictions with Grad-CAM++ with the aim to investigate to what extent the models focus on background elements that are known to be useful for human inspection. From this analysis we can conclude that the background cues are not relevant across all the attacks. Thus, showing the capability of the model to leverage the background information only when necessary.