Publications

@article{https://doi.org/10.1002/mp.70501,
   author = {Omena, Luana de M. and de Oliveira, Guilherme M. and do Rêgo, Thaís G. and Barbosa, Yuri de A. M. and Teixeira, Joao P. V. and Filho, Telmo M. Silva and Barufaldi, Bruno},
   title = {Improving textural realism in breast phantom images},
   journal = {Medical Physics},
   volume = {53},
   number = {5},
   pages = {e70501},
   keywords = {breast phantom, simplex noise, texture realism},
   doi = {https://doi.org/10.1002/mp.70501},
   url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.70501},
   eprint = {https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.70501},
   year = {2026}
 }

@inproceedings{choi2024comparative,
  author    = {Choi, C. J. and Barufaldi, B. and Teixeira, J. P. V. and
               Acciavatti, R. J. and Maidment, A. D. A.},
  title     = {{Comparative Evaluation of Ray-Tracing and Monte Carlo
               Virtual Clinical Trials Pipelines for Lesion Detection in
               Digital Breast Tomosynthesis}},
  booktitle = {Virtual Imaging Trials in Medicine},
  year      = {2024},
  doi       = {10.48550/arXiv.2405.05359}
}

@inproceedings{10.1117/12.3006839,
  author = {Bruno Barufaldi and Chloe J. Choi and Joao P. V. Teixeira and Magnus Dustler and Raphael B. Englander and Tha{\'i}s G. do R{\^e}go and Yuri Malheiros and Telmo M. Silva Filho and Belayat Hossain and Juhun Lee and Andrew D. A. Maidment},
  title = {{Representation of complex mammary parenchyma texture in tomosynthesis using simplex noise simulations}},
  volume = {12925},
  booktitle = {Medical Imaging 2024: Physics of Medical Imaging},
  editor = {Rebecca Fahrig and John M. Sabol and Ke Li},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {1292548},
  keywords = {Perlin noise, Simplex noise, breast complexity, breast cancer risk assessment, anthropomorphic phantoms},
  year = {2024},
  doi = {10.1117/12.3006839},
  URL = {https://doi.org/10.1117/12.3006839}
}

@Article{tomography9030092,
  AUTHOR = {Barufaldi, Bruno and da Nobrega, Yann N. G. and Carvalhal, Giulia and Teixeira, Joao P. V. and Silva Filho, Telmo M. and do Rego, Thais G. and Malheiros, Yuri and Acciavatti, Raymond J. and Maidment, Andrew D. A.},
  TITLE = {Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis},
  JOURNAL = {Tomography},
  VOLUME = {9},
  YEAR = {2023},
  NUMBER = {3},
  PAGES = {1120--1132},
  URL = {https://www.mdpi.com/2379-139X/9/3/92},
  PubMedID = {37368544},
  ISSN = {2379-139X},
  DOI = {10.3390/tomography9030092}
}

@inproceedings{dustler2023interactive,
  author    = {Dustler, M. and Tomic, H. and Bjerken, A. and Tingberg, A.
               and Timberg, P. and Zackrisson, S. and Costa, A. C. and
               Teixeira, J. P. V. and Barufaldi, B. and Bakic, P. R.},
  title     = {{Interactive Breast Lesion Designer for Virtual Trials Based
               on Perlin Noise}},
  booktitle = {Live Demonstrations Workshop, SPIE Medical Imaging},
  year      = {2023}
}

@inproceedings{10.1117/12.2626272,
   author = {Chloe J. Choi and Bruno Barufaldi and Jo{\~a}o P. V. Teixeira and Raymond J. Acciavatti and Andrew D. A. Maidment},
   title = {{Spatial dependency of lesion detectability in digital breast tomosynthesis}},
   volume = {12286},
   booktitle = {16th International Workshop on Breast Imaging (IWBI2022)},
   editor = {Hilde Bosmans and Nicholas Marshall and Chantal Van Ongeval},
   organization = {International Society for Optics and Photonics},
   publisher = {SPIE},
   pages = {1228618},
   keywords = {virtual clinical trials (VCT), tomosynthesis, lesion detection, image quality, digital breast tomosynthesis (DBT), Breast cancer, sensitivity, specificity},
   year = {2022},
   doi = {10.1117/12.2626272},
   URL = {https://doi.org/10.1117/12.2626272}
 }

@inproceedings{10.1117/12.2626225,
  author = {Yann N. G. da Nobrega and Giulia Carvalhal and Joao P. V. Teixeira and Barbara P. de Camargo and Thais G. do Rego and Yuri Malheiros and Telmo M. E. Silva Filho and Trevor L. Vent and Raymond J. Acciavatti and Andrew D. A. Maidment and Bruno Barufaldi},
  title = {{Multiclass segmentation of suspicious findings in simulated breast tomosynthesis images using a U-Net}},
  volume = {12286},
  booktitle = {16th International Workshop on Breast Imaging (IWBI2022)},
  editor = {Hilde Bosmans and Nicholas Marshall and Chantal Van Ongeval},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {122860L},
  keywords = {virtual clinical trial, anthropomorphic phantoms, artificial intelligence, risk stratified breast cancer screening, digital breast tomosynthesis, cancer masking, sensitivity, specificity},
  year = {2022},
  doi = {10.1117/12.2626225},
  URL = {https://doi.org/10.1117/12.2626225}
}

@inproceedings{10.1117/12.2612565,
  author = {Joao P. V. Teixeira and Telmo M. Silva Filho and Thais G. do Rego and Yuri B. Malheiros and Magnus Dustler and Predrag R. Bakic and Trevor L. Vent and Raymond J. Acciavatti and Srilalan Krishnamoorthy and Suleman Surti and Andrew D. A. Maidment and Bruno Barufaldi},
  title = {{Novel Perlin-based phantoms using 3D models of compressed breast shapes and fractal noise}},
  volume = {12031},
  booktitle = {Medical Imaging 2022: Physics of Medical Imaging},
  editor = {Wei Zhao and Lifeng Yu},
  organization = {International Society for Optics and Photonics},
  publisher = {SPIE},
  pages = {120313S},
  keywords = {virtual clinical trial, Perlin noise, digital breast tomosynthesis, ray-tracing},
  year = {2022},
  doi = {10.1117/12.2612565},
  URL = {https://doi.org/10.1117/12.2612565}
}

@InProceedings{10.1007/978-3-030-91699-2_18,
  author="de Paiva Rocha Filho, Itamar
  and Vasconcelos Teixeira, Jo{\~a}o Pedro
  and Lucena Lins, Jo{\~a}o Wallace
  and Honorato de Sousa, Felipe
  and Chaves Sousa, Ana Clara
  and Ferreira Junior, Manuel
  and Ramos, Tha{\'i}s
  and Silva, Cec{\'i}lia
  and do R{\^e}go, Tha{\'i}s Gaudencio
  and de Almeida Malheiros, Yuri
  and Silva Filho, Telmo",
  editor="Britto, Andr{\'e}
  and Valdivia Delgado, Karina",
  title="Iris-CV: Classifying Iris Flowers Is Not as Easy as You Thought",
  booktitle="Intelligent Systems",
  year="2021",
  publisher="Springer International Publishing",
  address="Cham",
  pages="254--264",
  abstract="The iris flower dataset is a ubiquitous benchmark task in machine learning literature. With its 150 instances, four continuous features, and three balanced classes, of which one is linearly separable from the others, iris is generally considered an easy problem. Hence researchers usually rely on other datasets when they need more challenging benchmarks. A similar situation happens with computer vision datasets such as MNIST and ImageNet, which have been widely explored. The state of the art models essentially solves these problems, motivating the search for more challenging tasks. Therefore, this paper introduces a new computer vision toy dataset featuring iris flowers. Users of a nature photography application took the pictures, thus they include noisy background information. Additionally, certain desirable features are not guaranteed, such as single, similarly-sized objects at the center of each picture, which makes the task more challenging. Our benchmark results show that the dataset can be challenging for traditional machine learning algorithms without any pre-processing steps, while state of the art deep learning architectures achieve around 82{\%} accuracy, which means some effort will be necessary to drive this accuracy closer to what has been accomplished for MNIST and ImageNet.",
  isbn="978-3-030-91699-2"
}