Guest Editorial Introduction to the Special Section on Weakly-Supervised Deep Learning and Its Applications

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Data Science

Background:

• Biomedical data analysis faces challenges due to noisy, limited, or imprecise expert annotations.
• Weakly-supervised deep learning (WSDL) offers a solution to reduce the manual annotation burden.
• WSDL enables deep neural networks to learn from large-scale datasets with reduced annotation costs.

Discussion:

• The integration of advanced deep learning techniques like generative adversarial networks (GANs), graph neural networks (GNNs), vision transformers (ViTs), and deep reinforcement learning (DRL) is crucial for WSDL.
• These advanced models are being explored to solve complex WSDL problems in biomedical data analysis.
• The focus is on leveraging these powerful AI tools to overcome annotation limitations.

Key Insights:

• WSDL is a vital approach for overcoming annotation scarcity in biomedical data.
• Deep learning models can be trained effectively on large datasets with minimal expert input.
• The development of WSDL methods is accelerating the application of AI in healthcare.

Outlook:

• Future research will likely focus on refining WSDL algorithms for even greater accuracy and efficiency.
• The application of WSDL is expected to expand across various biomedical domains, including medical imaging and signal processing.
• Continued advancements in AI will further enhance the capabilities of WSDL in biomedical engineering.