5 papers
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…
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…
Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis
Sabeen Ahmed, Nathan Parker, Margaret Park +8
Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (…
Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia
Sabeen Ahmed, Nathan Parker, Margaret Park +5
Cancer cachexia is a multifactorial syndrome characterized by progressive muscle wasting, metabolic dysfunction, and systemic inflammation, leading to reduced quality of life and i…
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…