In recent years, deep learning has been spotlighted as the most active research field with its great success in various machine learning communities, such as image analysis, speech recognition, and natural language processing, and now its promising potential is being actively discussed in the field of biomedicine. In particular, a dramatically increasing number of deep learning-based approaches have been proposed in biomedical images and signal processing. However, relatively little application of deep learning has been made in other biomedical areas such as genomics and computational biology due to the difficulty of definition and interpretation of deep learning architecture. Moreover, there are still many challenging tasks with many open problems in deep learning that need to be solved for active use in bioinformatics, biomedicine, and healthcare informatics.
The 7th International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H) will be held in conjunction with IEEE International Conference on Bioinformatics and Biomedicine (BIBM) at Decenber 5 - 8, 2023.
The goal of this workshop is to bring together researchers with expertise of deep learning in bioinformatics, biomedicine, and healthcare informatics and share current cutting-edge deep learning methodologies and its applications. Papers are welcome from the following topics (but not limited to):
Protein structures
Gene expression regulations
Genome-wide association studies
Protein function prediction
DNA-protein binding site identification
Clustering cancer subtypes
Single-cell clustering
Cancer diagnosis
3D brain reconstruction (MRI/fMRI)
Tissue image classification/Organ segmentation
Anomaly detection
Human activity recognition
Human behavior monitoring
Important Dates (Tentative)
Full workshop paper submission due: October 10, 2023
Notification of paper acceptance: November 5, 2023
Camera-ready due of accepted papers: November 21, 2023