New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations
Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi, Vishwa S. Parekh
MAFUS: a Framework to predict mortality risk in MAFLD subjects
Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Fedelucio Narducci, Tommaso Di Noia, Eugenio Di Sciascio