Martin Rajchl | DeepAI

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∙ 11/20/2018

Weakly Supervised Estimation of Shadow Confidence Maps in Ultrasound Imaging

Detecting acoustic shadows in ultrasound images is important in many cli...

20 Qingjie Meng, et al. ∙

∙ 07/18/2018

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Alterations in the geometry and function of the heart define well-establ...

8 Carlo Biffi, et al. ∙

∙ 06/14/2018

Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

Recent advances in deep learning led to novel generative modeling techni...

4 Xiaoran Chen, et al. ∙

∙ 06/11/2018

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

NeuroNet is a deep convolutional neural network mimicking multiple popul...

8 Martin Rajchl, et al. ∙

∙ 11/18/2017

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

We present DLTK, a toolkit providing baseline implementations for effici...

0 Nick Pawlowski, et al. ∙

∙ 11/04/2017

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consiste...

0 Konstantinos Kamnitsas, et al. ∙

∙ 11/03/2017

Implicit Weight Uncertainty in Neural Networks

We interpret HyperNetworks within the framework of variational inference...

0 Nick Pawlowski, et al. ∙

∙ 10/25/2017

Human-level CMR image analysis with deep fully convolutional networks

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging mo...

0 Wenjia Bai, et al. ∙

∙ 03/07/2017

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several c...

0 Ira Ktena, et al. ∙

∙ 11/22/2016

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion art...

0 Amir Alansary, et al. ∙

∙ 06/03/2016

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encoun...

0 Martin Rajchl, et al. ∙

∙ 05/25/2016

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

In this paper, we propose DeepCut, a method to obtain pixelwise object s...

0 Martin Rajchl, et al. ∙

∙ 01/30/2015

A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances

One issue limiting the adaption of large-scale multi-region segmentation...

0 John S. H. Baxter, et al. ∙

∙ 05/05/2014

A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization

The incorporation of region regularization into max-flow segmentation ha...

0 John S. H. Baxter, et al. ∙

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