3 citations · 9 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…
Improving Prostate Gland Segmentation Using Transformer based Architectures
Shatha Abudalou, Jung Choi, Yasin Yilmaz +1
Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transfor…
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…
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…
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran +2
Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improv…
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…