2 citations · 2 across the 5 of their papers we have counts for
7 papers
Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts
Aakash Tripathi, Ian E. Nielsen, Muhammad Umer +2
Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts…
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…
Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK
Carlos Garcia-Fernandez, Luis Felipe, Monique Shotande +6
Large language models (LLMs) show promise in healthcare, but hallucinations remain a major barrier to clinical use. We present CHECK, a continuous-learning framework that integrate…
TheBlueScrubs-v1, a comprehensive curated medical dataset derived from the internet
Luis Felipe, Carlos Garcia, Issam El Naqa +6
The need for robust and diverse data sets to train clinical large language models (cLLMs) is critical given that currently available public repositories often prove too limited in…
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…