Exploring Robustness of Neural Networks through Graph Measures. - Asim Waqas; Ghulam Rasool 001; Hamza Farooq; Nidhal C. Bouaynaya (CoRR, 2021)

Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review. - Asim Waqas; Aakash Tripathi; Ravi Prakash Ramachandran; Paul Stewart; Ghulam Rasool 001 (CoRR, 2023)

HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models. - Aakash Tripathi; Asim Waqas; Yasin Yilmaz; Ghulam Rasool 001 (CoRR, 2024)

SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology. - Asim Waqas; Aakash Tripathi; Sabeen Ahmed; Ashwin Mukund; Hamza Farooq; Matthew B. Schabath; Paul Stewart; Mia Naeini; Ghulam Rasool 001 (CoRR, 2024)

Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes. - Asim Waqas; Aakash Tripathi; Paul Stewart; Mia Naeini; Ghulam Rasool 001 (CoRR, 2024)

Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review. - Nikolas Koutsoubis; Asim Waqas; Yasin Yilmaz; Ravi P. Ramachandran; Matthew B. Schabath; Ghulam Rasool 001 (CoRR, 2024)

Multimodal data integration for oncology in the era of deep neural networks: a review. - Asim Waqas; Aakash Tripathi; Ravi Prakash Ramachandran; Paul A. Stewart; Ghulam Rasool 001 (Frontiers Artif. Intell., 2024)

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets. - Aakash Tripathi; Asim Waqas; Kavya Venkatesan; Yasin Yilmaz; Ghulam Rasool 001 (Sensors, 2024)

EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis. - Aakash Tripathi; Asim Waqas; Matthew B. Schabath; Yasin Yilmaz; Ghulam Rasool 001 (CoRR, 2025)