55 citations · 172 across the 25 of their papers we have counts for
5 papers · 1 filter
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…
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
Shahira Abousamra, Le Hou, Rajarsi Gupta +7
Deep learning classifiers for characterization of whole slide tissue morphology require large volumes of annotated data to learn variations across different tissue and cancer types…
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Han Le, Rajarsi Gupta, Le Hou +12
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and eva…
Label Super Resolution with Inter-Instance Loss
Maozheng Zhao, Le Hou, Han Le +8
For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology im…