1 citations · 1 across the 1 of their papers we have counts for
2 papers
q-bio.TO2025★ 1 cited
Explainable AI for computational pathology identifies model limitations and tissue biomarkers
Jakub R. Kaczmarzyk, Chanwoo Kim, Soham Gadgil +5
Deep learning models show promise in digital pathology, but their opaque decision-making processes limit trust and clinical adoption. To address this challenge, we present HIPPO, a…
eess.IV2025
Reusable specimen-level inference in computational pathology
Jakub R. Kaczmarzyk, Rishul Sharma, Peter K. Koo +1
Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built…