Workshop: The 5th Big Biomedical Data in Deep Learning Models

Call for Workshop Papers

Introduction

The field of biomedical research has witnessed an exponential increase in the generation and availability of large-scale datasets, commonly referred to as big biomedical data. These datasets offer invaluable insights into various aspects of human health and disease, and their analysis holds great potential for advancing biomedical research. Deep learning models, with their ability to capture complex patterns and extract meaningful features from large datasets, have emerged as powerful tools in harnessing the wealth of information present in big biomedical data. This conference aims to explore the intersection of big biomedical data and deep learning models, focusing on their applications, challenges, and future directions.

The scope of this workshop will encompass a wide range of topics related to the utilization of big biomedical data in the development and application of deep learning models. Key areas of interest include, but are not limited to:

Deep learning architectures for analyzing big biomedical data
Feature extraction and representation learning
Predictive modeling and clinical applications:
Interpretability and explainability of deep learning models
Challenges and ethical considerations

Workshop Paper Submission

Papers should be submitted through EAI ‘Confy+‘ system and have to comply with the Springer format (see Author’s kit section).

Short Workshop Papers should be up to 10 – 11 pages in length.
Full Workshop Papers should be up to 12-15 pages in length.

All conference papers undergo a thorough peer review process prior to the final decision and publication. This process is facilitated by experts in the Technical Program Committee during a dedicated conference period. Standard peer review is enhanced by EAI Community Review, which allows EAI members to bid to review specific papers. All review assignments are ultimately decided by the responsible Technical Program Committee Members, while the Technical Program Committee Chair is responsible for the final acceptance selection. You can learn more about Community Review here.

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