Ermanno Cordelli

Assistant Professor (RTDA) in the Computer Systems and Bioinformatics Lab, he has been at the Campus Bio-Medico University in Rome since 2006.

He is currently researching in different fields of Artificial Intelligence in general, Machine Learning and Deep Learning, such as Precision Medicine, Bit-omics and Embedded Systems with a special focus on Federated Learning.

He has a production of more than 30 scientific articles.

Education:

-(1 January 2022 – current)

Assistant Professor – Università Campus Bio-Medico di Roma – Department of Engineering – Unit of Computer Systems & Bioinformatics.

Main topic: Federated Learning in non-communicable diseases

-(1 October 2019 – current)

Postdoctoral researcher – Università Campus Bio-Medico di Roma – Department of Engineering – Unit of Computer Systems & Bioinformatics.

Topics: Radiomics and Pathomics, machine learning

-(1 October 2018 – 30 June 2019)

Research contributor – Università Campus Bio-Medico di Roma – Department of Engineering – Unit of Computer Systems & Bioinformatics.

Topics: data scientist, app developer

-(1 November 2015 – 31 October 2018)

PhD student in Bioengineering and Biosciences, Biomedical Engineering curriculum, XXXI cycle, Department of Engineering, University Campus Bio-Medico of Rome. Supervisors: Prof. Paolo Soda.

-(1 January 2015 – 31 December 2015)

Research contributor – Università Campus Bio-Medico di Roma – Department of Engineering – Unit of Computer Systems & Bioinformatics.

Topics: image processing, development of cellular segmentation algorithms, machine learning application for methods of semantic deconvolution.

-(28 July 2014)

Passed the government exam and licensed as a professional engineer (Section A, Industrial Sector, Class ML-21 or 26/S – Biomedical Engineer).

-(May 23, 2014)

Master in Biomedical Engineering. – Università Campus Bio-Medico di Roma.

Thesis title: Planning and test of an analog measurement chain for the collection of contactless biological signals, with a graphical interface connection to microcontroller.

-(May 23, 2009)

Bachelor in Biomedical Engineering. – Università Campus Bio-Medico di Roma.

Thesis title: Computer automation to determine the light intensity in HEp-2 images.

International Journals

- Caruso, CM, Guarrasi, V., Cordelli, E. et al. (2022). A Multimodal Ensemble Driven by Multiobjective Optimization to Predict Overall Survival in Non-Small-Cell Lung Cancer. Journal of Imaging, 8(11), 298.

- Santucci, D., Faiella, E., Gravina, M., Cordelli, E., et al. (2022). CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI. Cancers, 14(19), 4574.

- Faiella, E., Vertulli, D., Esperto, F., Cordelli, E. et al. (2022). Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study. Tomography, 8(4), 2010-2019.

- M. Tortora, E. Cordelli and P. Soda (2022). PyTrack: a Map-Matching-based Python Toolbox for Vehicle Trajectory Reconstruction.

- M. Tortora, E. Cordelli et al. (2022). RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy. arXiv preprint arXiv:2204.12423.

- R. Stefanucci, D. Santucci, SM Rossi, M. Samarra, E, Faiella, E. Cordelli et al. (2022). A Case of Secretory Carcinoma in a Patient With a History of Contralateral Medullary Carcinoma. Ultrasound, 1(2), 8-10.

- V. Guarrasi, NC D'Amico, R. Sicily, E. Cordelli and P. Soda (2022). Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. Pattern Recognition, 121, 108242.

- D. Santucci, E. Faiella, E. Cordelli et al. (2021). 3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients. Cancers, 2228, 13092228.

- P. Soda, NC D'Amico, J. Tessadori, G. Valbusa, V. Guarrasi, C. Bortolotto, MU Akbar, R. Sicilia, E. Cordelli et al. (2021). AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Medical image analysis, 102216.

- D. Santucci, E. Faiella, E. Cordelli et al. (2021). The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study. Cancers, 13(18), 4635.

- M. Tortora, E. Cordelli et al. (2021) Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. Artificial intelligence in medicine.

- E. Cordelli, P. Soda and G. Iannello (2021). Visual4DTracker: a tool to interact with 3D+t image stacks. BMC bioinformatics, 22(1), 1-15.

- D'Amico, NC, Sicily, R., Cordelli, E. et al. (2020). Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. Applied Sciences, 10(18), 6425.

- NC D'Amico, M. Merone, R. Sicilia, E. Cordelli et al. (2019) Tackling imbalance radiomics in acoustic neuroma. In International Journal of Data Mining and Bioinformatics (IJDMB).

- D'Amico, NC, Sicily, R., Cordelli, E. et al. (2019). Early radiomics experiences in predicting CyberKnife response in acoustic neuroma. ACM SIGBIO Newsletter, 8(3), 11-13.

- S. Ramella, M. Fiore, C. Greco, E. Cordelli, et al. (2018). A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients. In PloS one.

- E. Cordelli et al. (2018). A decision support system for type 1 diabetes mellitus diagnosis based on dual channel analysis of red blood cell membrane fluidity. In Computer Methods and Programs in Biomedicine (CMPB), volume 162, pages 263–271.