IEEE International Conference on Data Mining

1st Workshop on Emerging Trends in Deep Learning for Healthcare (ETDLH 2024)

Workshop on Emerging Trends in Deep Learning for Healthcare (ETDLH 2024) is dedicated to exploring the forefront of deep learning technologies tailored specifically for healthcare applications. As deep learning continues to evolve, its impact on healthcare becomes increasingly profound, offering transformative solutions for medical image analysis, disease diagnosis, personalized treatment recommendations, and healthcare management. This workshop aims to bring together researchers, practitioners, and industry experts to share their latest findings, exchange innovative ideas, and discuss the challenges and opportunities in leveraging deep learning for healthcare.

Deep learning has demonstrated remarkable capabilities in various domains of healthcare. In medical image analysis, deep learning models, particularly convolutional neural networks (CNNs), have set new benchmarks for accuracy and efficiency in tasks such as image classification, segmentation, and anomaly detection. The ability to process and analyze vast amounts of imaging data with unprecedented precision has paved the way for early disease detection and improved diagnostic workflows.

In the realm of disease diagnosis and prognosis, deep learning algorithms are proving to be indispensable tools. These models can uncover complex patterns within large datasets, enabling more accurate predictions of disease progression and patient outcomes. This is particularly crucial in managing chronic conditions and planning long-term treatment strategies. Moreover, deep learning is playing a pivotal role in the development of predictive models for personalized medicine, where treatments can be tailored to the individual characteristics of each patient, enhancing the efficacy of medical interventions.

Healthcare data analytics and decision support systems are also benefiting from deep learning advancements. By integrating deep learning with electronic health records (EHRs) and other healthcare databases, it is possible to develop robust systems that assist clinicians in making informed decisions. These systems can analyze patient data in real-time, providing insights that support clinical decision-making and optimize patient care.

However, the deployment of deep learning in healthcare is not without challenges. Ensuring the interpretability and explainability of deep learning models is critical for gaining the trust of healthcare professionals and patients. The complexity of these models often makes it difficult to understand how they arrive at certain conclusions, which can be a significant barrier to their adoption in clinical settings. Addressing these issues requires the development of methods that make deep learning models more transparent and interpretable.

Ethical considerations and regulatory challenges also play a crucial role in the deployment of deep learning solutions in healthcare. Protecting patient privacy, ensuring data security, and adhering to regulatory standards are paramount concerns that must be addressed to facilitate the widespread adoption of these technologies.

ETDLH 2024 will provide a platform for participants to delve into these topics and more. The workshop will feature keynote presentations, technical sessions, and panel discussions led by experts in the field. Attendees will have the opportunity to engage with leading researchers and industry practitioners, fostering collaborations that could drive future innovations in deep learning for healthcare.

Call for Papers (CFP): We invite submissions of original research papers (max 8 pages plus 2 extra pages) addressing, but not limited to, the following topics:

• Deep Learning Models for Medical Image Analysis:

  • Advanced convolutional neural networks (CNNs) for imaging.

  • Application of generative adversarial networks (GANs) in medical imaging.

  • Automated image segmentation and classification.

• Deep Learning Applications in Disease Diagnosis and Prognosis:

  • Early detection and prediction of diseases using DL models.

  • Prognostic modeling for chronic diseases.

  • Integrating multi-omics data for comprehensive disease understanding.

• Deep Learning-Based Predictive Modeling for Personalized Medicine:

  • Tailoring treatments based on genetic, environmental, and lifestyle factors.

  • Predictive analytics for treatment outcomes.

  • Adaptive learning systems for real-time treatment adjustments.

• Deep Learning Approaches for Healthcare Data Analytics and Decision Support Systems:

  • Integration of deep learning with electronic health records (EHRs).

  • Real-time data analysis and clinical decision support.

  • Predictive maintenance of healthcare infrastructure.

• Interpretability and Explainability of Deep Learning Models in Healthcare:

  • Techniques for model interpretability.

  • Case studies on explainable AI in clinical practice.

  • Enhancing trust and adoption of AI technologies.

• Ethical Considerations and Challenges in Deploying Deep Learning Solutions in Healthcare Settings:

  • Data privacy and security issues.

  • Regulatory compliance and ethical standards.

  • Addressing biases in deep learning models.

We look forward to receiving your contributions and welcoming you to an engaging and enlightening workshop at ETDLH 2024.

Important Dates

• Submission Deadline     September 10, 2024

• Notification to Authors   October 7, 2024

• Camera-ready Deadline 

and Copyright Forms   October 11, 2024

• Workshop day          December 9, 2024

Workshop Chairs

Technical Program Committee

• Yongyun Cho, Sunchon National University, South Korea

• Nithya Kandasamy, SRM University, India

• Ling Mee Hong, Sunway University, Malaysia

• C R Dhivyaa, VIT University, India

• Veningston K, National Institute of Technology, India

• Kottilingam Kottursamy, Madras Institute of Technology, India

• Fadi Al-Turjman, Near East University, Nicosia, Turkey

• Nallappan Gunasekaran, Beibu Gulf University, China

• Achyut Shankar, University of Warwick, United Kingdom

• Ali Ahmadian, Mediterranea University of Reggio Calabria, Italy

• Nebojsa Bacanin, Singidunum University, Belgrade, Serbia

• Mohamed Abouhawwash, Michigan State University, USA

• Vladimir Simic, University of Belgrade, Serbia

• Ali Kashif Bashir, Manchester Metropolitan University, U.K.

• Durga Prasad Bavirisetti, Norwegian University of Science and Technology, Norway

• Saravana Balaji B, De Montfort university Kazakhstan, Kazakhstan

• Prabhu E, Amrita Vishwa Vidyapeetham University, India

• S.S.Askar, King Saud University, Saudi Arabia

• S.Kannimuthu, Karpagam College of Engineering, India

• Senthilkumar M, VIT University, India

• Neelakandan Subramani, Hanyang University, South Korea

• Thameem Basha H, Ulsan National Institute of Technology, South Korea

You can contact us at sathishv@sunway.edu.my

The workshop will be held in conjunction with IEEE International Conference on Data Mining (ICDM), 9-12 December 2024, Abu Dhabi, UAE.