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Found 6 papers, 2 papers with code
Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training
We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology whole slide images (WSIs) for input to a neural network on-the-fly.
A tool for user friendly, cloud based, whole slide image segmentation
As an extension of our previous work (arXiv:1812. 07509), we have developed a tool for segmentation of whole slide images (WSIs) with an easy to use graphical user interface.
Neural Network Segmentation of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis in Renal Biopsies
Glomerulosclerosis, interstitial fibrosis, and tubular atrophy (IFTA) are histologic indicators of irrecoverable kidney injury.
Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology
We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship.
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs.
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection
The proposed MCD method outperformed a popular spectral clustering based method in performing FLIM image segmentation.
