Koninklijke Philips Corporation N.v.

Nederlandse Organisatie Voor Toegepast-natuurwetenschappelijk Onderzoek

Bo Liu

Gereon Vogtmeier

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Johannes Wilhelmus Maria Jacobs

Junyi Liu

Sandeep Unnikrishnan

Erina Ghosh

Dorothee Christine Hermes

Aaditya Prakash

Eric Thomas Carlson

David Paul Noren

Mohammad Shahed Sorower

Andre Yaroshenko

A method () for training a scoring system () comprising the steps of: (i) providing () a scoring system comprising a scoring module (); (ii) receiving () a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Method and system for automated inclusion or exclusion criteria detection

The invention relates to a method of manufacturing a structured grating, a corresponding structured grating component () and an imaging system. The method comprising the steps of: providing () a catalyst () on a substrate (), the catalyst () having a grating pattern; growing () nanostructures () on the catalyst () so as to form walls () and trenches () based on the grating pattern; and filling () the trenches () between the walls () of nanostructures () using an X-ray absorbing material (). The invention provides an improved method for manufacturing a structured grating and such structured grating component (), which is particularly suitable for dark-field X-ray imaging or phase-contrast imaging.

Structured grating component, imaging system and manufacturing method

The present disclosure pertains to a paraphrase generation system. The system comprises one or more hardware processors and/or other components. The system is configured to obtain a training corpus. The training corpus comprises language and known paraphrases of the language. The system is configured to generate, based on the training corpus, a word-level attention-based model and a character-level attention-based model. The system is configured to provide one or more candidate paraphrases of a natural language input based on both the word-level and character-level attention-based models. The word-level attention-based model is a word-level bidirectional long short term memory (LSTM) network and the character-level attention-based model is a character-level bidirectional LSTM network. The word-level and character level LSTM networks are generated based on words and characters in the training corpus. In some embodiments, the LSTM networks are stacked residual LSTM networks comprising residual connections between stacked layers of a given LSTM network.

Systems and methods for neural clinical paraphrase generation

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Cambridge, MA, United States of America

Bo Liu