412 citations · 795 across the 22 of their papers we have counts for
7 papers · 1 filter
ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology
Duncan Stothers, Ren-Chin Wu, William Lotter
Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inference requires applying a larg…
Anatomy Contextualized Adaptation of CT Foundation Models
Roshan Kenia, Stephanie L McNamara, William Lotter
CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-g…
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu +1
Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local expl…
How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology
Kian R. Weihrauch, Thomas A. Buckley, William Lotter +1
General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs). Because WSIs exceed contempo…
Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance
Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert +1
AI-based image reconstruction models are increasingly deployed in clinical workflows to improve image quality from noisy data, such as low-dose X-rays or accelerated MRI scans. How…
FDA AI Search: Making FDA-Authorized AI Devices Searchable
Arun Kavishwar, William Lotter
Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because…
Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations
Pritika Vig, Ren-Chin Wu, William Lotter
Vision foundation models trained on discretely sampled images achieve strong performance on classification benchmarks, yet whether their representations encode the continuous proce…