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<p>Dear colleagues,</p>
<p>We are organizing a Special Session on "Advances in Deep Learning
for Biomedical Data Analysis" at the International Joint
Conference on Neural Networks (IJCNN 2025,
<a class="moz-txt-link-freetext" href="https://2025.ijcnn.org/">https://2025.ijcnn.org/</a>), to be held in Rome, Italy, from 30 June
to 5 July 2025. </p>
<p>We would appreciate if you forward this CfP to friends and
colleagues that might be interested.</p>
<p>///////////////////////////////////////////////////////////</p>
<p><b>SPECIAL SESSION on Advances in Deep Learning for Biomedical
Data Analysis</b></p>
<p>=== Scope and Topics ===</p>
<p>Biomedical data analysis involves the treatment of the
physiological electrical activities measured using sensors placed
on a living thing, also the medical imaging, allowing it to
provide a useful process for abnormality detection and diagnosis
purposes. Recently, Deep Learning (DL) has received a great
attention to solve difficult and complex problems related to
biosignal and medical image processing, where the traditional
signal/image processing algorithms and conventional machine
learning techniques have shown their limitations to solve such
problems. Indeed, the recent advances in this area have brought
impressive progress to solve several practical and difficult
problems in many fields including medicine, healthcare, e-health,
neuroscience, brain-computer interface (BCI), neurofeedback,
robotics, robotic exoskeletons, and biometrics, etc. In this
context, advanced DL models, have shown their effectiveness to
resolve various problems of detection, classification, clustering,
prediction, segmentation, diagnosis, etc.; thus, becomes useful
solutions to be investigated more for other open problems.</p>
<p>The aim of this special session is to bring together researchers
and scientists in the fields of biomedical signal and image
processing, artificial intelligence and artificial learning, to
present and discuss the recent advances in DL algorithms and
methods applied for biomedical data processing.</p>
<p>The main topics that are of interest to this special session
include, but are not limited to:</p>
- DL for biomedical signal analysis and processing (e.g., EEG, ECG,
EMG, EOG, …)<br>
- DL for medical image analysis and processing (e.g., MRI, X-ray,
PET scan, CT scan, …)<br>
- DL for diseases detection and diagnosis<br>
- DL for pandemics detection and forecasting<br>
- DL for biometrics<br>
- DL for health informatics<br>
- DL for e-health<br>
- DL for brain-computer interfaces<br>
- Related applications<br>
<p>=== ORGANIZERS ===</p>
Larbi Boubchir<br>
<i>Full Professor</i><br>
University of Paris 8, France<br>
<br>
Boubaker Daachi<br>
<i>Full Professor</i><br>
University of Paris 8, France<br>
<br>
<p>=== SUBMISSION GUIDELINE ===</p>
<p>Prospective authors are invited to submit complete papers of no
more than eight (8) pages in the IEEE two-column conference
proceedings format. Please follow the submission guideline from
the IJCNN 2025 submission website.</p>
<p>Special session papers are treated the same as regular conference
papers. Please specify that your paper is for the Special Session
on Advances in Deep Learning for Biomedical Data Analysis.</p>
<p>All the accepted and presented papers will be published on IEEE
Xplore Digital Library and indexed by Scopus.</p>
<p>=== AUTHORS INFORMATIONS ===</p>
- Author instructions:
<a class="moz-txt-link-freetext" href="https://2025.ijcnn.org/authors/initial-author-instructions">https://2025.ijcnn.org/authors/initial-author-instructions</a><br>
- Paper submission guidelines:
<a class="moz-txt-link-freetext" href="https://cmt3.research.microsoft.com/IJCNN2025/">https://cmt3.research.microsoft.com/IJCNN2025/</a><br>
- Further information is available at: <a class="moz-txt-link-freetext" href="https://2025.ijcnn.org/">https://2025.ijcnn.org/</a><br>
<p>=== CONTACT ===</p>
<p>Any inquiries can be directed to Prof. Larbi Boubchir :
<a class="moz-txt-link-abbreviated" href="mailto:larbi.boubchir@univ-paris8.fr">larbi.boubchir@univ-paris8.fr</a></p>
<p>///////////////////////////////////////////////////////////</p>
Best regards,<br>
Prof. Larbi Boubchir<br>
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