Special Session: Quantum Machine Learning for Rare Disease Identification
We propose a special session focused on “Quantum Machine Learning for Rare Disease Identification” to be published in 16th IEEE International Conference on Knowledge and Systems Engineering (KSE 2024). This special session aims to bring together cutting-edge research and advancements at the intersection of quantum computing, machine learning, and rare disease diagnostics. Rare diseases, due to their low prevalence, present unique challenges in diagnosis and treatment. Quantum Machine Learning (QML) offers promising solutions to these challenges through its potential to process vast amounts of data more efficiently than classical methods.
Scope and Topics:
The special session will cover a broad range of topics within the realm of quantum machine learning
and its applications to rare disease identification, including but not limited to:
- Quantum algorithms for medical diagnostics
- Quantum-enhanced machine learning techniques
- Case studies on QML applications in rare disease detection
- Comparative studies between classical and quantum approaches in healthcare
- Quantum data processing methods for large-scale biological data
- Ethical implications and challenges in QML for healthcare
- Future directions and potential of QML in personalized medicine
- Integration of quantum computing with existing healthcare infrastructure
Importance:
The identification and diagnosis of rare diseases remain a significant challenge due to the scarcity of data and the complexity of medical conditions. QML provides a novel approach that could revolutionize this field by offering new ways to analyse complex datasets, identify patterns, and predict outcomes with higher accuracy and speed. This special session aims to highlight the latest research, foster collaboration, and stimulate further advancements in this emerging field.
We believe that this special session will significantly contribute to the growing body of knowledge in the field of quantum machine learning and its application in healthcare, specifically for the identification and diagnosis of rare diseases. We look forward to receiving innovative and impactful
contributions from researchers and practitioners worldwide.
Important Dates: (GMT +8:00) See Important Dates
- Full Paper Deadline:
15 June 2024 30 July 2024 (Rigid Deadline)
- Acceptance Notification: 20 September 2024
- Camera-ready Paper Deadline: 23 October 2024
Submission Guidelines:
Authors are invited to submit papers of up to 6 pages, written in English, in PDF format and
compliant with the IEEE standard (https://www.ieee.org/conferences/publishing/templates.html), via the KSE 2024 submission page https://submit.confbay.com/sub?view=submit&acid=1465 and select the Sub-theme: “Special Session QML 2024: Quantum Machine Learning for Rare Disease Identification“. The submissions will be peer-reviewed for originality and scientific
quality.
Authors of selected papers from the special session will be invited to submit an extended and
improved version to a Special Issue published in the CMC-Computers, Materials & Continua (Scopus,
ESCI – Q2): https://www.techscience.com/cmc/special_detail/AI_cancer-diagnostics
Session Organizers:
- Fahad Ahmad, University of Portsmouth, UK, fahad.ahmad@port.ac.uk
- Kashaf Junaid, Queen Mary university of London, UK, kashaf.junaid@qmul.ac.uk
For any inquiries regarding this special session, please refer to the above contacts.
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