152 citations · 180 across the 8 of their papers we have counts for
8 papers
EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis
Aakash Tripathi, Asim Waqas, Matthew B. Schabath +2
Accurate cancer survival prediction requires integration of diverse data modalities that reflect the complex interplay between imaging, clinical parameters, and textual reports. Ho…
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…
Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets
Aakash Tripathi, Asim Waqas, Kavya Venkatesan +2
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopatho…
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran +2
Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical rec…
Exploring Robust Architectures for Deep Artificial Neural Networks
Asim Waqas, Ghulam Rasool, Hamza Farooq +1
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a…