Connectionists: CFP: Special track on Explainable Machine Learning models in Medical Imaging @CBMS 2021

[Alba García]

Call for papersSpecial track on Explainable Machine Learning models in
Medical Imaging

Computational medical imaging techniques aim towards enhancing the
diagnostic performance of visual assessments in medical imaging, improving
the early diagnosis of various diseases, helping to obtain a deeper
understanding of physiology and pathology, and therefore contributing to
advance the field of Quantitative Radiology. To reach these goals, medical
image computing and signal processing are commonly combined with
biophysical models, which describe explicitly the organ or tissue under
investigation.

Machine Learning (ML) models revolutionised multiple tasks in medical image
computing, such as image segmentation, registration and synthesis, through
the extensive analysis of big imaging data. Although ML models outperform
classic approaches on the above tasks, they remain to a large extend
implicit in terms of describing the data under investigation. This limits
ML model interpretability, which in turn is one of the main barriers
towards ML-based pathology detection assessments and generalised single- or
multi-modal ML analysis in medical imaging. Moreover, in modern clinical
practices and settings, detailed explanations of the model behaviours are
increasingly required to support reliability towards improving clinical
decision making. Model explainability becomes also critical when data
integration techniques are required to cross-assess learning performance
from imaging data against mutual, complementary or “clinical reference
standard” information from “additional modalities” (either imaging, or
other types of biomedical/clinical data such as invasive methods or *ex
vivo *analysis, and/or other). To support further development of ML models
for clinical applications, model explainability is highly important towards
enhancing generalisability, trustworthiness, causality, transferability,
informativeness, confidence, accessibility and interactivity. Last not
least, being of the most promising topics in ML/medical imaging research,
the main challenge for developing explainable models is to improve
explainability whilst maintaining high learning performances.

The main objective of this special issue is to attract original
high-quality research and survey articles that reflect the most recent
advanced on explainable ML models in medical imaging (MRI, CT, PET, SPECT,
Ultrasound and other), by investigating novel methodologies either through
interpreting algorithm components and/or exploring algorithm-data
relationships.

We welcome researchers from both academia and industry, to present their
state-of- the-art scientific developments, technologies, and ideas covering
all possible aspects of explainable ML models in medical imaging.
Topics of interest include (but are not limited to):

·         Develop and interpret ML models in single- or multi-modal (MRI,
CT, Ultrasound, PET, SPECT) imaging (using either single- or
multiple-inputs and thus, biophysical information)

·         To improve explainability, combine ML with biophysical modelling
and/or visual assessments from additional/complementary imaging modalities
(e.g. multiple sequences in MRI, or combining MRI with Ultrasound, CT, PET
or SPECT)

·         To improve explainability, combine ML with other types of
“reference standard” input data (e.g. clinical data, electrophysiology
signals, molecular analysis, invasive methods) that can enhance ML
interpretability in medical imaging

·         Enhance explainability through combining multiple tasks (e.g.
segmentation and/ or image synthesis)/ Multi-task learning on
multi-modality medical images

·         Enhance explainability by incorporating graphical deep learning
models for single- or multi-modality image analysis

·         Solidify explainability in cross-domain image synthesis between
different imaging modalities or sequences (e.g. from different MRI
sequences, or MRI and CT, etc.)

·         Transfer learning and transferability for single- or
multi-modality medical images

·         ML model explainability in semi-supervised, weakly-supervised and
unsupervised learning in medical imaging

·         Post-hoc explainability techniques for ML models in single- or
multi-modality images

·         Enhance explainability through developing ML models to detect or
predict pathology versus healthy statuses

·         Explain strengths and weaknesses of ML models through
quantitative evaluation and interpretation of algorithm performances
Paper submission guidelines

Authors are invited to submit their original contributions before the
deadline following the conference submission guidelines. Each contribution
must be prepared following the IEEE two-column format, and should not
exceed the length of 6 (six) Letter-sized pages. For detailed instructions
please visit: https://cbms2021.web.ua.pt/

All submissions will be peer-reviewed by three reviewers of the Program
Committee. All accepted papers will be included in the conference
proceedings, and will be published by the IEEE. For each accepted paper, at
least one author must register at the conference before the Author
Registration Deadline. Publication in proceedings is conditioned to the
registration and presentation of the paper at the conference by one of
their authors. If the paper is not presented at the conference, it will not
be included in the proceedings.

Authors of the best papers will be invited to submit an extended
contribution to a journal special issue.
Important dates

·         *Paper submission deadline: *February 5, 2021

·         *Notification of acceptance:* March 26, 2021

·         *Camera-ready due:* April 16, 2021

·         *Registration*

·         *Early registration deadline:* April 16, 2021

·         *Conference: *June 7, 2021
More informationWebpage: https://essexnlip.uk/cbms2021/ Special Track Chairs

·         Dr Giorgos Papanastasiou
<https://www.essex.ac.uk/people/papan14104/giorgos-papanastasiou>,
University of Essex, UK [g.papanastasiou(at)essex.ac.uk]

·         Dr Alba García Seco de Herrera
<https://www.essex.ac.uk/people/GARCI58409/alba-garcia-seco-de-herrera>,
University of Essex, UK [alba.garcia(at)essex.ac.uk]

·         Dr Chengjia Wang <https://chengjiawang.github.io/>, University of
Edinburgh, UK [Chengjia.Wang(at)ed.ac.uk]

·         Professor Heye Zhang, Sun Yat-sen University, China [ZhangHeye(at)
mail.sysu.edu.cn]

·         Dr Guang Yang <https://www.imperial.ac.uk/people/g.yang>,
Imperial College London, UK [G.Yang(at)imperial.ac.uk]
Program committee members

Sotirios A. Tsaftaris, University of Edinburgh, UK

Gabriele Valvano, IMT Lucca, Italy

Victor Gonzalez-Castro, University of Leon, Spain

Lin Gu, RIKEN AIP, University of Tokyo, Japan

Hao Dong, Peking University, China

Zhangming Niu, Aladdin Healthcare Technologies, Germany

Chunliang Wang, KTH Royal Institute of Technology, Sweden

Sivarama Krishan Rajaraman, NIH/NLM, USA

Emanuele Trucco, University of Dundee, UK

George Matsopoulos, National Technical University of Athens, Greece

David Rodriguez Gonzalez, University of Cantabria, Spain

Sammy Danso, University of Edinburgh

Xurui Jin, Duke Kunshan University, China-USA

Adrian Clark, University of Essex, UK

Adrian Martín Fernández, Pompeu Fabra University, Spain

Eirini Christinaki, KU Leuven, Belgium

Oscar Jiménez del Toro, University of Applied Sciences Western Switzerland,
Switzerland

Rakkrit Duangsoithong, Prince of Songkla University, Thailand







Dr Alba García Seco de Herrera PhD

Lecturer

Department of Computer Science and Electronic Engineering (CSEE)

University of Essex



T +44 (0) 1206 872907

E alba.garcia at essex.ac.uk

► https://www.essex.ac.uk/
<https://www.essex.ac.uk/people/garci58409/alba-garcia>



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