25 citations · 48 across the 6 of their papers we have counts for
6 papers
Open and reusable deep learning for pathology with WSInfer and QuPath
Jakub R. Kaczmarzyk, Alan O'Callaghan, Fiona Inglis +5
The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologist…
AI and Pathology: Steering Treatment and Predicting Outcomes
Rajarsi Gupta, Jakub Kaczmarzyk, Soma Kobayashi +2
The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses. We describe the rich s…
Evaluating histopathology transfer learning with ChampKit
Jakub R. Kaczmarzyk, Tahsin M. Kurc, Shahira Abousamra +3
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopat…
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Mahmudul Hasan, Jakub R. Kaczmarzyk, David Paredes +9
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been d…
Knowing what you know in brain segmentation using Bayesian deep neural networks
Patrick McClure, Nao Rho, John A. Lee +7
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was tra…
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub R. Kaczmarzyk +5
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated b…