no code implementations • 2 Oct 2020 • Zohaib Salahuddin, Matthias Lenga, Hannes Nickisch
A similar multi-scale dual pathway 3D CNN is trained to identify coronary artery endpoints for terminating the tracking process.
no code implementations • 23 Jan 2020 • Ivo M. Baltruschat, Leonhard Steinmeister, Hannes Nickisch, Axel Saalbach, Michael Grass, Gerhard Adam, Tobias Knopp, Harald Ittrich
Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.
no code implementations • 25 Jun 2019 • Moti Freiman, Hannes Nickisch, Holger Schmitt, Pal Maurovich-Horvat, Patrick Donnelly, Mani Vembar, Liran Goshen
We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data.
no code implementations • 24 Jun 2019 • Moti Freiman, Hannes Nickisch, Sven Prevrhal, Holger Schmitt, Mani Vembar, Pál Maurovich-Horvat, Patrick Donnelly, Liran Goshen
Purpose: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm from coronary computed tomography angiography (CCTA).
no code implementations • 28 Oct 2018 • William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions.
no code implementations • 17 Oct 2018 • Ivo M. Baltruschat, Leonhard Steinmeister, Harald Ittrich, Gerhard Adam, Hannes Nickisch, Axel Saalbach, Jens von Berg, Michael Grass, Tobias Knopp
Chest radiography is the most common clinical examination type.
no code implementations • 6 Mar 2018 • Ivo M. Baltruschat, Hannes Nickisch, Michael Grass, Tobias Knopp, Axel Saalbach
The increased availability of X-ray image archives (e. g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques.
no code implementations • ICML 2018 • Hannes Nickisch, Arno Solin, Alexander Grigorievskiy
We provide a comprehensive overview and tooling for GP modeling with non-Gaussian likelihoods using state space methods.
3 code implementations • NeurIPS 2017 • Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.
no code implementations • 18 Dec 2015 • Vlado Menkovski, Zharko Aleksovski, Axel Saalbach, Hannes Nickisch
Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant.
no code implementations • 13 Nov 2015 • William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.
3 code implementations • 5 Nov 2015 • Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch
This multi-level circulant approximation allows one to unify the orthogonal computational benefits of fast Kronecker and Toeplitz approaches, and is significantly faster than either approach in isolation; 2) local kernel interpolation and inducing points to allow for arbitrarily located data inputs, and $O(1)$ test time predictions; 3) exploiting block-Toeplitz Toeplitz-block structure (BTTB), which enables fast inference and learning when multidimensional Kronecker structure is not present; and 4) projections of the input space to flexibly model correlated inputs and high dimensional data.
3 code implementations • 3 Mar 2015 • Andrew Gordon Wilson, Hannes Nickisch
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs).
no code implementations • IEEE Transactions on Pattern Analysis and Machine Intelligence 2013 • Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object’s color or shape.
1 code implementation • NeurIPS 2011 • David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
We introduce a Gaussian process model of functions which are additive.
no code implementations • NeurIPS 2008 • Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf, Matthias Seeger
We propose a novel scalable variational inference algorithm, and show how powerful methods of numerical mathematics can be modified to compute primitives in our framework.