55 citations · 172 across the 25 of their papers we have counts for
26 papers
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…
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…
ImageBox3: No-Server Tile Serving to Traverse Whole Slide Images on the Web
Praphulla MS Bhawsar, Erich Bremer, Máire A Duggan +4
Whole slide imaging (WSI) has become the primary modality for digital pathology data. However, due to the size and high-resolution nature of these images, they are generally only a…
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc +6
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as…
Run-time Parameter Sensitivity Analysis Optimizations
Eduardo Scartezini, Willian Barreiros, Tahsin Kurc +4
Efficient execution of parameter sensitivity analysis (SA) is critical to allow for its routinely use. The pathology image processing application investigated in this work processe…
Exascale Deep Learning to Accelerate Cancer Research
Robert M. Patton, J. Travis Johnston, Steven R. Young +9
Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural n…