Abstract submission for posters are now closed. The following themes were available:

Theme ①: In Silico and Real World Biomarker Discovery and AI

Covering topics from methodology to application to verification.

• Cancer biomarker discovery — use cases and applications of AI
• Biomarker data (knowledge) Integration:  imaging, radiomics, high dimensional data (Proteomics’s, genomics, epigenetics, etc.)
• Showcasing datasets for AI/ML — real world data, synthetic data, and simulated data

Theme ②: Biomarker Computation and Methodology Considerations for applying AI

• Uncertainty, statistical rigor, bias onsiderations in AI/ML
• Statistical methods vs AI considerations: optimal approaches to enable data analysis
• Trust in ML methodologies for cancer biomarkers: explainability, reproducibility, interpretability of results

Theme ③: Considerations in Data Preparation, Sharing, and Analysis

• Creating AI ready datasets: data preparation, harmonization, and standardization
• Making data usable: data capture, sharing, federation, and scalable computation for cancer biomarkers  
• Using open science to link AI algorithms and datasets to publications
• Ethical considerations with data and AI/ML

Theme ④: Emerging Capabilities and Methodologies in AI

• Novel and emerging methods for image analysis
• LLMs/foundation models, generative AI, federation/federated learning

Theme ⑤: Academic-Industry Partnerships in AI and Bioinformatics

• Technology transfer
• Structuring mutually beneficial relationships