95 citations · 365 across the 12 of their papers we have counts for
13 papers
Talking-Heads Attention
Noam Shazeer, Zhenzhong Lan, Youlong Cheng +2
We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the…
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
Le Hou, Rajarsi Gupta, John S. Van Arnam +5
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale,…
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…
High Resolution Medical Image Analysis with Spatial Partitioning
Le Hou, Youlong Cheng, Noam Shazeer +6
Medical images such as 3D computerized tomography (CT) scans and pathology images, have hundreds of millions or billions of voxels/pixels. It is infeasible to train CNN models dire…
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…