152 citations · 180 across the 8 of their papers we have counts for
4 papers · 1 filter
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review
Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz +3
Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease di…
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
Asim Waqas, Aakash Tripathi, Paul Stewart +3
Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especial…
Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology
Asim Waqas, Aakash Tripathi, Sabeen Ahmed +6
Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling t…
HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models
Aakash Tripathi, Asim Waqas, Matthew B. Schabath +2
HONeYBEE (Harmonized ONcologY Biomedical Embedding Encoder) is an open-source framework that integrates multimodal biomedical data for oncology applications. It processes clinical…