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A method for normalizing histology slides for quantitative analysis
This paper provides two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.
Fast Global Labeling for Real-Time Stereo Using Multiple Plane Sweeps
The proposed method is applied to accelerate the cleanup step of a real-time dense stereo method based on plane sweeping with multiple sweeping directions, where the label set directly corresponds to the employed directions.
Deep Learning with Topological Signatures
This work proposes a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training, realized as a novel input layer with favorable theoretical properties.
Restoration of DWI Data Using a Rician LMMSE Estimator
This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images.
Geodesic Regression for Image Time-Series
A generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration, which allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values.
Segmentation of Knee Images: A Grand Challenge
An evaluation framework for the 3D segmentation of knee bones and cartilage from magnetic resonance images is presented and the motivation for this challenge is described and the preparation of training and test datasets are described.
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
A deep learning framework that jointly learns networks for image registration and image segmentation, which achieves large simultaneous improvements of segmentation and registration accuracy (over independently trained networks) and allows training high-quality models with very limited training data.