From the 1 of 11 linked papers with an AI index.
11 papers
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…
Anatomy Contextualized Adaptation of CT Foundation Models
Roshan Kenia, Stephanie L McNamara, William Lotter
The paper proposes Anatomy Contextualized Adaptation (ACA), a lightweight method that adapts frozen CT vision-language foundation models to align anatomy-level visual features with…
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…
AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics
Stella Su, Marc Harary, Scott J. Rodig +1
Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations. However, the robustness of standard SSL methods to…
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…