The possibility of planning the right therapy for the right patient is the main focus of precision medicine research. In this context artificial intelligence, machine learning and deep learning are more and more gaining the scene thanks to the increased availability of heterogeneous medical data collected from healthcare systems. Indeed ‘-omics’ sciences, such as radiomics, genomics and pathomics, with their combinations (radiogenomics, radiopathomics, etc.) represent emerging approaches aiming at quantifying tumour phenotype. Specifically, the combination of clinical data and features extracted from radiological images, histological data and genomic information, together with AI and statistical techniques are exploited for prognostic and predictive ends. Specifically, they can generate insights to improve the discovery of new therapeutic procedures, to enhance and optimize current treatments, to help increasing the patients quality of life, just to name a few.
This special track aims to bring together researchers and practitioners, who are dealing with recent advances in artificial intelligence techniques for -omics sciences, providing the opportunity to promote, present, stimulate and facilitate the discussion of ongoing work in this this wide and multidisciplinary landscape (methodologies, practices, strategies, modeling, tools, latest results). This special track can also provide a forum to present latest research results, new technology developments, new strategies, and new applications in this area.
The topics include, but are not limited to:
- Radiomic image analysis
- Genomic and proteomic data analysis
- Pathomic image analysis
- Multi -omics predictive systems (radiogenomics, radiogenopathomics, etc.)
- Deep learning for -omics sciences
- Reinforcement learning for -omics sciences
- Explainable AI systems for -omics sciences
- Exposome data mining
- Tumour segmentation and radiomic signature variability
- Prospective authors are invited to submit papers in any of the topics listed above.
- Instructions for preparing the manuscript (in Word and Latex formats) are available at: Call for Papers (main track)
- Please also check the Guidelines.
- Papers must be submitted electronically via the web-based submission system.
- Bradley J Erickson, MD PhD, Department of Radiology, Mayo Clinic, USA
- Ermanno Cordelli, PhD, Università Campus Bio-Medico di Roma, Italy
- Rosa Sicilia, PhD, Università Campus Bio-Medico di Roma, Italy
Program Committee (to be confirmed)
- Marco Aiello, IRCCS Fondazione SDN, Italy
- Michele Avanzo, CRO Centro di Riferimento Oncologico di Aviano, Italy
- Angel Alberich-Bayarri, Polytechnic University of Valencia, Spain
- Luca Brunese, Università degli Studi del Molise, Italy
- Di Dong, Chinese Academy of Sciences, China
- Yong Fan, University of Pennsylvania, USA
- Elisa Ficarra, Politecnico di Torino, Italy
- Qiao Huang, Mayo Clinic, USA
- Anant Madabhushi, Case Western Reserve University, USA
- Alexis Andrew Miller, Illawarra Cancer Care Centre, Australia
- Hangfan Liu, University of Pennsylvania, USA
- Giuseppe Perrone, Università Campus Bio-medico di Roma, Italy
- Sara Ramella, Università Campus Bio-medico di Roma, Italy
- Carlo Sansone, Università degli Studi di Napoli, Italy
- Joseph Stancanello, Guerbet SA, France
- Linlin Shen, Shenzhen University, China
- Jie Tian, Institute of Automation Chinese Academy of Sciences, China
- Giovanni Valbusa, Centro Diagnostico Italiano, Italy
- Wim Van Hecke, Icometrix, Belgium
- Pierangelo Veltri, Università Magna Graecia di Catanzaro, Italy
A journal special issue will be proposed to authors with accepted papers for submitting an extended version of their work.
Easychair platform for submissions: https://easychair.org/conferences/?conf=cbms2021
Ermanno Cordelli, email@example.com
Rosa Sicilia, firstname.lastname@example.org
Università Campus Bio-Medico di Roma – Italy