Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Philips Intellectual Property & Standards Gmbh

Michael Grass

Bernardus Hendrikus Wilhelmus Hendriks

Raoul Florent

Jens Von Berg

Alexandra Groth

Christian Haase

Arjen Van Der Horst

Robert Johannes Frederik Homan

Axel Saalbach

Peter Maria Johannes Rongen

Javier Olivan Bescos

Vincent Maurice André Auvray

Harold Agnes Wilhelmus Schmeitz

Ronaldus Frederik Johannes Holthuizen

Romane Isabelle Marie-Bernard Gauriau

Bram Antonius Philomena Van Rens

Roland Wilhelmus Maria Bullens

Martijn Anne Van Lavieren

Thirukumaran Thangaraj Kanagasabapathi

Holger Schmitt

Ivo Matteo Baltruschat

Dirk Schaefer

Christian Buerger

Christian Wuelker

Jürgen Erwin Rahmer

Ming De Lin

Tobias Klinder

Cristian Lorenz

Nikolas David Schnellbaecher

Dirk SCHÄFER

Sebastian Wild

Eran Langzam

Thomas Koehler

Frank Bergner

Bernhard Gleich

Kevin Martin Brown

Sven Prevrhal

André Goossen

Wade Allen Bowe

Roland Proksa

Hannes Nickisch

Rolf Dieter Bippus

Tanja Elss

Axel Thran

Eberhard Sebastian Hansis

Cornelis Willem Johannes Immanuël Spoel

Sandra Simon Halliburton

Mordechay Pinchas Freiman

Yechiel Lamash

Hanno Heyke Homann

Volker Rasche

Liran Goshen

Thomas Heiko Stehle

Hans Christian Haase

Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (I) by labeling the first image data (I) and determining second synthesized labeled image data (I) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (I) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.

Multi-task deep learning method for a neural network for automatic pathology detection

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface () and a processing unit (). The input interface is configured to obtain image data () representing a two-dimensional representation of a vessel (). The processing unit () is configured to determine a course of the vessel () and a width (w, w) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

Apparatus for determining a functional index for stenosis assessment

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BUCHHOLZ IN DER NORDHEIDE, Netherlands

Michael Grass