Víctor M. Campello

Orcid: 0000-0003-1727-983X

According to our database1, Víctor M. Campello authored at least 25 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2026
Uncertainty-fetal head and pubic symphysis segmentation with enhanced multi-scale features and sparse visual graph attention.
Expert Syst. Appl., 2026

2025
Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare.
J. Heal. Informatics Res., September, 2025

Federated learning in low-resource settings: A chest imaging study in Africa - Challenges and lessons learned.
CoRR, May, 2025

ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from low-income countries.
Medical Image Anal., 2025

Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353].
Medical Image Anal., 2025

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images.
Medical Image Anal., 2025

Segment Anything Model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis.
Expert Syst. Appl., 2025


2024
Fetal Head and Pubic Symphysis Segmentation in Intrapartum Ultrasound Image Using a Dual-Path Boundary-Guided Residual Network.
IEEE J. Biomed. Health Informatics, August, 2024

Generalizability in multi-centre cardiac image analysis with machine learning
PhD thesis, 2024

Efficient MedSAMs: Segment Anything in Medical Images on Laptop.
CoRR, 2024

Democratizing AI in Africa: FL for Low-Resource Edge Devices.
CoRR, 2024

SpeChrOmics: A Biomarker Characterization Framework for Medical Hyperspectral Imaging.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.
IEEE J. Biomed. Health Informatics, July, 2023

2022
Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.
Medical Image Anal., 2022

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries.
CoRR, 2022

Domain generalization in deep learning for contrast-enhanced imaging.
Comput. Biol. Medicine, 2022

Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
IEEE Trans. Medical Imaging, 2021

Multi-center, multi-vendor automated segmentation of left ventricular anatomy in contrast-enhanced MRI.
CoRR, 2021

Style Curriculum Learning for Robust Medical Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020
Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge.
Proceedings of the Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images, 2020

2019
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 2019


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