4 papers
Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Mayanka Chandrashekar, Xi Zhang, Ethan Seefried +3
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At sc…
In-Domain Supervised Pathology Report Classification: A Reproducible Pipeline from Data Curation to Production-Matched Evaluation
Isaac Hands, Bin Huang, Adam Spannaus +4
We introduce an in-domain supervised pipeline designed to counter the out-of-distribution performance drop that hampers supervised biomedical NLP models, a problem observed when mo…
Resource-Adaptive Federated Text Generation with Differential Privacy
Jiayi Wang, John Gounley, Heidi Hanson
In cross-silo federated learning (FL), sensitive text datasets remain confined to local organizations due to privacy regulations, making repeated training for each downstream task…
Can human clinical rationales improve the performance and explainability of clinical text classification models?
Christoph Metzner, Shang Gao, Drahomira Herrmannova +1
AI-driven clinical text classification is vital for explainable automated retrieval of population-level health information. This work investigates whether human-based clinical rati…