Search Results for author: Simon A. A. Kohl

Found 9 papers, 7 papers with code

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

no code implementations28 Nov 2019 David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.

Anomaly Detection

Reg R-CNN: Lesion Detection and Grading under Noisy Labels

2 code implementations22 Jul 2019 Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl, Klaus H. Maier-Hein

To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.

General Classification

Deep Probabilistic Modeling of Glioma Growth

1 code implementation9 Jul 2019 Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.

Representation Learning

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

3 code implementations30 May 2019 Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger

Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.

Instance Segmentation Medical Image Segmentation +1

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

4 code implementations17 Apr 2019 Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.

Medical Image Segmentation Semantic Segmentation

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

5 code implementations21 Nov 2018 Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt, Fabian Isensee, Tristan Anselm Kuder, Heinz-Peter Schlemmer, Klaus H. Maier-Hein

The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.

Medical Object Detection Semantic Segmentation +1

A Probabilistic U-Net for Segmentation of Ambiguous Images

7 code implementations NeurIPS 2018 Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger

To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.

Decision Making Semantic Segmentation

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