3 papers
q-bio.TO2025
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…
q-bio.TO2025
Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models
Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo +5
Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast cancer but are not universally 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…