M3IP 2023

WELCOME to M3IP 2023

First International Workshop on Multi-Modal Medical Imaging Processing (M3IP 2023)

In conjunction with ICIAP 2023 

Udine September 11-15, 2023

INTRODUCTION

In recent years, novel imaging modalities including multi-slice, multi-parametric, and multi-modal (hybrid) tools have been introduced in medical practices as a result of ongoing technological advancements in image acquisition. Medical image computing refers to the process of extracting relevant information from medical images with the aim of developing non-invasive biomarkers for the characterization of the area under analysis. In the context of clinical practice, various instruments, methods of measurement, and experimental setups can be employed to gather information pertaining to specific phenomena or systems of interest, such as organs or tissues. Due to the heterogeneity and complexity of natural environments and processes, it is rare for a single acquisition technique to provide a comprehensive understanding of these phenomena. Indeed, the utilization of multiple sources enables a deeper comprehension of the system under investigation, enhances the decision-making process, and facilitates the identification of relationships between different data modalities. 

The large amount of information to consider, and the high complexity of medical images, which make the manual inspection a very tedious and hard task, have prompted research into proposing solutions for the automatic analysis of radiological acquisitions. Artificial Intelligence (AI), and in particular Machine Learning (ML) and Deep Learning (DL) approaches, had a radical spread in medical image computing with surprising results.  Moreover, the increasing use of different diagnostic tools in clinical practice results in the development of AI-based solutions in applications characterized by the need of leveraging information coming from multimodal data sources. The idea is that heterogeneous images may highlight inherent aspects of the area under analysis that are useful for its characterization. Multimodal Learning allows the fusion of complementary information coming from heterogeneous sources with the aim of providing a richer data representation than the unimodal approach. When investigated in conjunction with deep networks, multimodal learning is also known as Multimodal Deep Learning (MDL), leveraging the ability of the deep neural networks to provide an effective high-level representation of the input.

The purpose of Multi-Modal Medical Imaging Processing (M3IP) workshop is to disseminate the recent developments of MDL approaches in medical imaging analysis. In particular, it focuses on the integration of heterogeneous sources of data for different applications, enabling the communication and interaction between researchers in medical imaging processing with expertise in data fusion methods and multimodal learning.  

TOPIC

Topics of interest include, but are not limited to: 

• Innovative methodologies for fusing data from different scanners (e.g. CT/PET/MRI)

• Integration of medical imaging and clinical data

• Multi-protocol processing

• Exploiting heterogeneous information for precision and personalised medicine

• Multimodal disease classification, prediction, and progression

• Multimodal image segmentation

• Spatial-temporal analysis using multiple modalities

• Multimodal image acquisition and reconstruction

• Biomarkers identification exploiting multimodal data

• Cross-modality image generative methods (e.g generation of synthetic images from MRI and PET)

• Multimodal image co-registration

• Multimodal bit-omics (e.g. radiomics, genomics, etc.)

• Explainable artificial intelligence in multimodal medical image processing

• Trustworthy AI in medicine: privacy, fairness, and equity in medical data processing and fusion

• Secure storage and processing of medical data in multimodal scenario (including attack and defense strategy)

• Multimodal information systems

• Multimodal information retrieval

Important dates

• Paper Submission Deadline: July 7th, 2023  July 15th, 2023

• Paper notification: July 30th, 2023

• Camera-ready paper due: August 15th, 2023 

• Workshop date: September 11th, 2023 

Paper submission

Submitted papers must be unpublished and not considered elsewhere for publication.  Submissions will undergo a rigorous review process handled by the Technical Program Committee. Papers will be selected through a single-blind review process with at least 3 reviews for each paper, based on their originality, significance, relevance, and clarity of presentation. Papers must be in English, up to 12 pages in Springer format, including references and appendices. Submissions with a number of pages between 6 and 8 will be considered as short papers.  

All submissions will be handled electronically via the conference’s CMT Website:
https://cmt3.research.microsoft.com/ICIAPMIH2023/
Authors can find complete instructions of how to format their papers at
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
The papers selected as best papers will be invited to submit an extended version to the Special Issue Application of Deep Learning and Convolution Neural Networks for Social Healthcare
The best paper will be invited to submit an extended version with waived publishing charges

Workshop Chairs

• Prof. Angel García-Pedrero, Ph.D., Universidad Politecnica de Madrid

• Eng. Michela Gravina, Ph.D., University of Naples Federico II

• Eng. Stefano Marrone   Ph.D., University of Naples Federico II

Program Committee Member

• Prof. Vincenzo Moscato, University of Naples "Federico II", Italy

• Prof. Paolo Soda, University Campus Bio-Medico Roma, Italy

• Prof. Consuelo Gonzalo-Martín, Universidad Politécnica de Madrid

• Prof. Mario Sansone, University of Naples "Federico II", Italy

• Prof. Francesco Tortorella, University of Salerno, Italy

• Prof. Domenico Parmeggiani, University of Campania Luigi Vanvitelli

• Prof. Peter Antal, Budapest University of Technology and Economics

• Prof. Armando Marino, University of Stirling

• Dr. Rossella Arcucci, Imperial College London

• Eng. Ermanno Cordelli, University Campus Bio-Medico Roma, Italy

• MD Domiziana Santucci, University Campus Bio-Medico Roma, Italy

• MD Giuseppe Pontillo, University of Naples "Federico II", Italy

• MD Gianluca Gatta, Università della Campania “L.Vanvitelli”

• MD Antonella Sciarra, Università della Campania “L.Vanvitelli”

• Eng. Giuseppe Fiameni, NVIDIA AI Technology Center Italy

• Prof. Francesco Flammini, Linnaeus University 

Technical Sponsor

The M3IP workshop will be sponsored by MathWorks

KEYNOTE SPEECH

Dr George Amarantidis from MathWorks

Contacts

• Prof. Angel García-Pedrero, angelmario.garcia@upm.es

• Eng. Michela Gravina,  michela.gravina@unina.it

• Eng. Stefano Marrone,  stefano.marrone@unina.it

M3IP 2023 Udine, Italy, September 11-15 2023