Search Results for author: Hannes Nickisch

Found 16 papers, 4 papers with code

Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans

no code implementations2 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.

Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation

no code implementations23 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.

Learning a sparse database for patch-based medical image segmentation

no code implementations25 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.

Image Segmentation Medical Image Segmentation +2

Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation

no code implementations24 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).

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

no code implementations28 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.

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

no code implementations6 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.

Classification General Classification +1

State Space Gaussian Processes with Non-Gaussian Likelihood

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.

Gaussian Processes

Scalable Log Determinants for Gaussian Process Kernel Learning

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.

Gaussian Processes Point Processes

Can Pretrained Neural Networks Detect Anatomy?

no code implementations18 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.

Anatomy speech-recognition +1

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

no code implementations13 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.

Gaussian Processes

Thoughts on Massively Scalable Gaussian Processes

3 code implementations5 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.

Gaussian Processes

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

2 code implementations3 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).

Gaussian Processes

Attribute-Based Classification for Zero-Shot Visual Object Categorization

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.

Classification Object Categorization +2

Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

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.

Bayesian Inference Experimental Design +1

Cannot find the paper you are looking for? You can Submit a new open access paper.