Multi-task Library to Develop Computer-Aided Tools for Medical Data Analysis¶
Framework for development and analysis of deep neural network architectures applied to medical data (images, 2D and 3D). This package can be readily used on a number of public datasets. It can be extended to add more datasets, and models.
Use one or more the BibTeX references below to cite this work:
@INPROCEEDINGS{raposo_union_2022,
author = {Raposo, Geoffrey and Trajman, Anete and Anjos, Andr{\'{e}}},
month = 11,
title = {Pulmonary Tuberculosis Screening from Radiological Signs on Chest X-Ray Images Using Deep Models},
booktitle = {Union World Conference on Lung Health},
year = {2022},
date = {2022-11-01},
organization = {The Union},
}
@TECHREPORT{Raposo_Idiap-Com-01-2021,
author = {Raposo, Geoffrey},
keywords = {deep learning, generalization, Interpretability, transfer learning, Tuberculosis Detection},
projects = {Idiap},
month = {7},
title = {Active tuberculosis detection from frontal chest X-ray images},
type = {Idiap-Com},
number = {Idiap-Com-01-2021},
year = {2021},
institution = {Idiap},
url = {https://gitlab.idiap.ch/biosignal/software/mednet},
pdf = {https://publidiap.idiap.ch/downloads/reports/2021/Raposo_Idiap-Com-01-2021.pdf}
}
@INPROCEEDINGS{renzo_2021,
title = {Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results},
author = {Matheus A. Renzo and Nat\'{a}lia Fernandez and Andr\'e Baceti and Natanael Nunes de Moura Junior and Andr\'e Anjos},
month = {10},
booktitle = {XV Brazilian Congress on Computational Intelligence},
year = {2021},
url = {https://publications.idiap.ch/index.php/publications/show/4649},
}
@MISC{laibacher_2019,
title = {On the Evaluation and Real-World Usage Scenarios of Deep Vessel Segmentation for Retinography},
author = {Tim Laibacher and Andr\'e Anjos},
year = {2019},
eprint = {1909.03856},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1909.03856},
}