As a postdoctoral researcher in the Bioinformatics and Cellular Genomics laboratory and part of the BRAIx collaboration, I use cutting-edge Artificial Intelligence (AI) and data science approaches to develop software able to accurately diagnose breast cancer on mammogram scans.

My PhD focused on using AI to characterise organs in a post-mortem computed tomography database in the Department of Medical Imaging and Radiation Sciences at Monash University. I started working on image processing during my Master’s studies in Engineering (Industrial Automation), using machine vision and several machine learning techniques for robotic applications.

I have obtained a high level of experience in using deep learning techniques, such as convolutional neural networks for localisation, feature extraction, and data representation. During my PhD and postdoctoral appointment, I extended my knowledge of deep learning to implement different network architectures such as VGG, ResNet, and convolutional autoencoders for supervised, semi-supervised, and unsupervised learning.

I have more than seven years’ experience in programming using Matlab, Python and Pytorch, including the use of specialised libraries such as OpenCV and scikit-learn. In addition, I have experience in the use of high-performance data processing facilities, utilising Unix-based systems, GPUs, and computing clusters for large-scale data analysis.

Key achievements

2016-2019

  • Computational Biomedicine PhD Scholarship

2017

  • Victorian Orthopaedic Research Trust

2018

  • Australian Synchrotron Beamtime

2021

  • SVI Rising Star Award

Selected publications

Frazer, H, Peña-Solorzano, C. A., Kwok, C. F., Elliott, M., Chen, Y, Wang, C., the BRAIx team, Lippey, J., Hopper, J., Brotchie, P., Carneiro, G., McCarthy, D. J. (2022). AI integration improves breast cancer screening in a real-world, retrospective cohort study. medRxiv 2022.11.23.22282646; doi: doi.org/10.1101/2022.11.23.22282646

Peña-Solórzano, C. A., Albrecht, D. W., Harris, P. C., Bassed, R. B., Gillam, J., Dimmock, M. R. (2020). Semi-supervised labelling of the femur in a whole-body post-mortem CT database using deep learning. Computers in Biology and Medicine, 122, 103797; doi.org/10.1016/j.compbiomed.2020.103797

Peña-Solórzano, C. A., Albrecht, D. W., Bassed, R. B., Burke, M. D., Dimmock, M. R. (2020). Findings from machine learning in clinical medical imaging applications – lessons for translation to the forensic setting. Forensic Science International. 316, 110538; doi.org/10.1016/j.forsciint.2020.110538

Peña‐Solórzano, C. A., Albrecht, D. W., Paganin, D. M., Harris, P. C., Hall, C. J., Bassed, R. B., & Dimmock, M. R. (2019). Development of a simple numerical model for trabecular bone structures. Medical physics, 46(4), 1766-1776; doi.org/10.1002/mp.13435

Peña-Solórzano, C. A., Dimmock, M. R., Albrecht, D. W., Paganin, D. M., Bassed, R. B., Klein, M., & Harris, P. C. (2018). Effect of external fixation rod coupling in computed tomography. Strategies in Trauma and Limb Reconstruction, 1-13; doi.org/10.1007/s11751-018-0318-x

ORCID profile: https://orcid.org/0000-0002-1734-7284

Google Scholar profile: https://scholar.google.com/citations?user=hIm0mdYAAAAJ&hl=en

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