Special Issue on Advanced Image Analysis and Processing for Biomedical Applications

July 7th, 2020 Daniela Lopez de Luise
A special issue of Applied Sciences (Scopus CiteScore: 2.4;  Impact
Factor 2,217; ISSN 2076-3417). This special issue belongs to the section
“Computing and Artificial Intelligence”.

https://www.mdpi.com/journal/applsci/special_issues/Image_Analysis_Processing

Deadline for manuscript submissions: 30 November 2020.

Dear Colleagues,

Medical images contain plenty of information about the anatomical
structures that are important for a valid diagnosis, and then can be
helpful to doctors for determining the most adequate treatment. The
analysis and processing of biomedical images is an interdisciplinary and
dynamic area of specialization, covering biology, physics, medicine,
engineering, and computer science. The main objective is the application
of image processing and analysis techniques to biological or medical
problems. For instance, in biomedicine, it is possible to use
computational methods of image processing and analysis to model and
visualize human organs from medical images. These methods can have
different objectives, such as enhanced visualization, 3D reconstruction,
segmentation, motion and deformation analysis, and registration.

This Special Issue on Advanced Image Analysis and Processing for
Biomedical Applications aims to provide an assorted and complementary
collection of contributions showing new advancements and applications of
advanced imaging analysis and processing in the biomedical imaging area.
The ultimate objective is to promote research and advancement in the
field, by publishing high-quality research articles and reviews in this
rapidly growing interdisciplinary field.

Topics of interest include, but are not limited to, the following:

Image enhancement, segmentation, registration, and fusion for biomedical
applications;
Image acquisition and processing for biomedical applications;
Reconstruction, motion, and deformation analysis for biomedical
applications;
Computer-aided diagnosis, surgery, therapy, treatment, and telemedicine
systems;
Application of machine learning and artificial intelligence in medicine;
Telemedicine systems for elderly care;
Mobile applications and low-cost systems;
Sparse representation and dictionary learning-based methods for medical
image processing and understanding;
Deep learning for biomedical image analysis;
Natural language processing for biomedical image analysis;
Bio-inspired contour detection;
Biomedical ultrasonics;
Fluorescence image analysis;
Cardiovascular image analysis;
Super-resolution microscopy;
Retinal image analysis;
Virtual surgery.

Guest Editors:
Francesco Isgrò, Andrea Apicella, Domenico Tegolo, Cesare Valenti,
Roberto Prevete

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Special Issue on “Advanced Machine Learning Algorithms for Biometrics and Its Applications”

July 7th, 2020 Daniela Lopez de Luise

Special Issue on “Advanced Machine Learning Algorithms for Biometrics and Its Applications”

Journal: Applied Sciences (ISSN 2076-3417).
Deadline for manuscript submissions: 31 December 2020.
URL:https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Biometrics
Flyer: https://www.mdpi.com/journal/applsci/special_issue_flyer_pdf/Machine_Learning_Biometrics/web

Dear colleagues,

Biometrics has become a burgeoning research area due to the industrial and government needs for recognition and security concerns. It has also become a center of focus for many applications, such as identity authentication and identification in civil and forensic fields. Recently, advanced machine learning has received a great deal of attention in solving difficult and complex problems related to biometric recognition and security, where conventional machine learning techniques have shown their limitations.

This Special Issue aims to solicit original research papers, as well as review articles focusing on biometrics and its applications based on advanced machine learning algorithms. We are inviting original research works covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the biometrics domain.

Topics of interest include but are not limited to the following:

    – Biometrics-based authentication and identification;
    – Physiological and behavioral biometrics (e.g., finger, palm, face, eye, ear, iris, retina, vein, gait, handwriting, voice);
    – Biometric feature extraction and matching;
    – Signal, image, and video processing in biometrics;
    – Advanced pattern recognition in biometrics;
    – Machine learning and deep learning in biometrics;
    – Artificial intelligence in biometrics;
    – Fusion techniques in biometrics;
    – Soft biometrics;
    – Multimodal biometrics;
    – Security and privacy in biometrics;
    – Big data challenges in biometrics;
    – Embedded biometric systems;
    – Emerging biometrics;
    – Related applications.

Assoc. Prof. Dr. Larbi Boubchir
Prof. Dr. Elhadj Benkhelifa
Prof. Dr. Boubaker Daachi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Special Issue Editors:

Assoc. Prof. Dr. Larbi Boubchir (larbi.boubchir@ai.univ-paris8.fr)
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics

Prof. Dr. Elhadj Benkhelifa (E.Benkhelifa@staffs.ac.uk)
Guest Editor
Cloud Computing and Applications Research Lab, School of Computing and Digital Technologies, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Interests: cloud computing; cybersecurity; software engineering; software defined systems; cloud forensics; IoT; data governance

Prof. Dr. Boubaker Daachi (bd@ai.univ-paris8.fr)
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: robotics; soft computing; BCI; WSN; biometrics

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International Workshop on pattern recognition for positive technology and elderly wellbeing (CARE2020)

July 7th, 2020 Daniela Lopez de Luise

“>SUBMISSIONS ARE OPEN!!! 
***********************************

Submission deadline    October 10th, 2020

Author notification        November 10th, 2020

FULL CALL FOR PAPERS IN ATTACHMENT

 

ABOUT CARE 2020 
***********************

Life expectancy horizon is in continuous growth, with an arising socio-economical needs of supporting the aging population. If this is stimulating a considerable research effort in the ICT field, there is a big gap between the complexity of available ICT devices and the needs of fragile individuals.

CARE2020 aims at bringing together the most recent advances of Intelligent Systems  for Positive Technologies and elderly wellbeing.

The list of relevant topics includes (but is not limited to) the following: 

  • Emotion recognition
  • Social interaction analysis
  • Facial expression analysis
  • Body gesture analysis
  • Human behaviour analysis
  • Physiological signal analysis
  • Ecological datasets
  • Mood induction
  • Virtual reality as a positive technology
  • Intelligent cognitive assistants
  • Activities of Daily Living recognition
  • Emotion-aware ambient intelligence
  • Natural Human-Computer Interaction


INVITED KEYNOTE SPEAKERS 
*************************************

  • Prof. Hatice Gunes (University of Cambridge, UK) Creating Technology with Socio-emotional Intelligence
  • Prof. Andrea Gaggioli (Università Cattolica di Milano, Italy) Positive AI: Opportunities and challenges of integrating artificial intelligence in digital wellbeing application

ORGANIZING COMMITTEE 
********************************

  • Raffaella Lanzarotti (Università degli Studi di Milano)
  • Nicoletta Noceti (Università degli Studi di Genova)
  • Claudio de'Sperati (Università Vita-Salute San Raffaele, Milano)
  • Francesca Odone (Università degli Studi di Genova)
  • Giuliano Grossi (Università degli Studi di Milano)


CONTACT
************

All questions about submissions should be emailed to care2020@di.unimi.it

Nicoletta Noceti, PhD
Assistant Professor in Computer Science
MaLGa – Machine Learning Genoa center https://ml.unige.it 
DIBRIS – Università di Genova, Italy
Tel. +39 010 3536704
#unigenonsiferma
pdf icon CARE2020-cfp.pdf
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iMIMIC at MICCAI 2020 [NEW DATES] – Workshop on Interpretability of Machine Intelligence in Medical Image Computing

July 7th, 2020 Daniela Lopez de Luise
CALL FOR PAPERS: iMIMIC @ MICCAI 2020 – Submission Deadline Extended
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020
iMIMIC 2020 workshop: October 4 2020, Lima, Peru, (https://imimic-workshop.com)
MICCAI 2020 conference: October 4-8, 2020, Lima, Peru, (https://www.miccai2020.org/)
OVERVIEW
The annual MICCAI conference attracts world leading biomedical scientists, engineers, and clinicians from a wide range of disciplines associated with medical imaging and computer-assisted intervention.
Machine learning (ML) systems are achieving remarkable performances at the cost of increased complexity. Hence, they become less interpretable, which may cause distrust. As these systems are pervasively being introduced to critical domains, such as medical image computing and computer-assisted intervention (MICCAI), it becomes imperative to develop methodologies to explain their predictions. Such methodologies would help physicians to decide whether they should follow/trust a prediction or not. Additionally, it could facilitate the deployment of such systems, from a legal perspective. Ultimately, interpretability is closely related with AI safety in healthcare.
However, there is very limited work regarding interpretability of ML systems among the MICCAI research. Besides increasing trust and acceptance by physicians, interpretability of ML systems can be helpful during method development. For instance, by inspecting if the model is learning aspects coherent with domain knowledge, or by studying failures. Also, it may help revealing biases in the training data, or identifying the most relevant data (e.g., specific MRI sequences in multi-sequence acquisitions). This is critical since the rise of chronic conditions has led to a continuous growth in usage of medical imaging, while at the same time reimbursements have been declining. Hence, improved productivity through the development of more efficient acquisition protocols is urgently needed.
The Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2020 aims at introducing the challenges & opportunities related to the topic of interpretability of ML systems in the context of MICCAI.
SCOPE
Interpretability can be defined as an explanation of the machine learning system. It can be broadly defined as global, or local. The former explains the model and how it learned, while the latter is concerned with explaining individual predictions. Visualization is often useful for assisting the process of model interpretation. The model’s uncertainty may be seen as a proxy for interpreting it, by identifying difficult instances. Still, although we can find some approaches for tackling machine learning interpretability, there is a lack of formal and clear definition and taxonomy, as well as general approaches. Additionally, interpretability results often rely on comparing explanations with domain knowledge. Hence, there is the need for defining objective, quantitative, and systematic evaluation methodologies.
Covered topics include but are not limited to:
– Definition of interpretability in the context of medical image analysis.
– Visualization techniques useful for model interpretation in medical image analysis.
– Local explanations for model interpretability in medical image analysis.
– Methods to improve transparency of machine learning models commonly used in medical image analysis.
– Textual explanations of model decisions in medical image analysis.
– Uncertainty quantification in the context of model interpretability.
– Quantification and measurement of interpretability.
– Legal and regulatory aspects of model interpretability in medicine.
IMPORTANT DATES
Submission Deadline: July 14 2020.
Notification of Acceptance: July 31 2020.
Camera-ready Deadline:  August 7 2020.
Workshop: October 4 2020.
KEYNOTE SPEAKERS
Himabindu Lakkaraju, Harvard University, USA.
Wojciech Samek, Fraunhofer HHI, Germany.
VENUE
The iMIMIC workshop will be held in the morning of 4 of October as a workshop of MICCAI 2020.
We would like to inform you that in light of the ongoing COVID-19 pandemic, the MICCAI 2020 Conference Organizing team and the MICCAI Society Board have decided to hold the MICCAI 2020 annual meeting planned for October 4-8, 2020 in Lima, Peru as a fully virtual conference. More information regarding the venue can be found at the conference website at (https://www.miccai2020.org/en/CONFERENCE-VENUE.html)
ADDITIONAL INFORMATION AND SUBMISSION DETAILS
Submissions must be original and not published elsewhere. Authors should prepare a manuscript of 8 pages, excluding references. The manuscript should be formatted according to the Lecture Notes in Computer Science (LNCS) style. All submissions will be reviewed by 3 reviewers. The reviewing process will be single-blinded. Authors will be asked to disclose possible conflict of interests, such as cooperation in the previous two years. Moreover, care will be taken to avoid reviewers from the same institution as the authors. The selection of the papers will be based on their relevance for medical image analysis, significance of results, technical and experimental merit, and clear presentation.
Authors should submit their articles in a single pdf file in the submission website – no later than July 14 2020.
Notification of acceptance will be sent by July 31 2020 and the camera-ready version of the papers revised according to the reviewers' comments should be submitted by August 7 2020.
We will join the MICCAI Satellite Events joint proceedings and publish the accepted papers as LNCS. We are also considering making the pre-print of the accepted papers publicly available.
ORGANIZING COMMITTEE
Jaime S. Cardoso, INESC TEC and University of Porto, Portugal.
Pedro H. Abreu, CISUC and University of Coimbra, Portugal.
Ivana Isgum, Amsterdam University Medical Center, The Netherlands
José P. Amorim, CISUC and University of Coimbra, Portugal – Publicity Chair
Wilson Silva, INESC TEC and University of Porto, Portugal – Program Chair
Ricardo Cruz, INESC TEC and University of Porto, Portugal – Sponsor Chair
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Deadlines Updated: ECCV2020 Workshop on Imbalance Problems in Computer Vision (IPCV)

July 7th, 2020 Daniela Lopez de Luise

Deadlines for the IPCV workshop at ECCV 2020 are updated. You can find the details below.

**ECCV Workshop on Imbalance Problems in Computer Vision (IPCV)**

28 August 2020, Glasgow

https://sites.google.com/view/ipcv2020/

**Dates:**

Paper submission: 12 July 2020. 7 July 2020.

Notification: 10 August 2020. 28 July 2020.

Camera ready: 17 August 2020. 7 August 2020. Video Uploads: 21 August 2020.

Workshop: 28 August 2020

**Workshop Theme and Scope**

Performance of learning-based methods is adversely affected by imbalance problems at various levels, including the input, intermediate or mid-level stages of the processing or the objectives to be optimized in a multi-task setting. Currently, researchers tend to address these challenges in their particular context with problem-specific solutions and with limited awareness of the solutions proposed for similar challenges in other computer vision problems.

Imbalance problems can arise in  almost all computer vision problems and therefore, the workshop is highly relevant and interesting for a broad community. A recent, comprehensive review paper on imbalance problems in object detection (IEEE TPAMI, 2020; preprint: https://arxiv.org/abs/1909.00169)  cites over 200 papers which were written by 655 unique authors. We interpret these numbers (which are specific to just one computer vision task, namely, object detection) as strong indicators of interest in imbalance problems.

We invite contributions for (i) the dissemination of approaches developed in individual problems, as well as (ii) discussing commonalities between these approaches for developing better and more general solutions for addressing imbalance problems in computer vision.

**Paper Submission**

Paper template and length: Please follow ECCV2020 format and guidelines.

Submission link: https://openreview.net/group?id=thecvf.com/ECCV/2020/Workshop/IPCV

**Proceedings:** 

Accepted papers will be included in ECCV2020 Workshop Proceedings.

**Presentation Information**

All accepted papers will have a spotlight presentation followed by a poster session.

**Organizers**

Sinan Kalkan, University of Cambridge; Middle East Technical University

Emre Akbas, Middle East Technical University

Nuno Vasconcelos, University of  California San Diego

Baris Can Cam, Middle East Technical University

Kemal Oksuz, Middle East Technical University


Baris.

YanıtlaYönlendir

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