Koninklijke Philips Corporation N.v.

Versuni Holding B.v.

Binyam Gebrekidan Gebre

Karl Catharina Van Bree

Frederik Jan De Bruijn

Yong Jiang

Mohankumar Valiyambath Krishnan

Dmitry Nikolayevich Znamenskiy

Aditya Mehendale

Victor Martinus Gerardus Van Acht

Arnoldus Johannes Martinus Jozeph Ras

Antonius Johannes Joseph Wismans

Petra Bijl

Sergio Consoli

Orhan Kahya

Yao Hean Chiah

Dimitrios Mavroeidis

Harold Agnes Wilhelmus Schmeitz

Christine Menking Swisher

Erik Bresch

Teun Van Den Heuvel

Boon Teck Tan

Yen Leng Pang

Ulf Grossekathöfer

Wee Ann Chong

Stojan Trajanovski

Hee Poh Sin

Harold Agnes Willhelmus Schmeitz

Xin Xian Kong

Kenny Khng

Ze He

Kai Wong

Dmitry Nilolayevich Znamenskiy

Various embodiments of the present disclosure are directed to a salient medical imaging controller () employing an artificial intelligence engine () and a graphical user interface (). In operation, the artificial intelligence engine () includes one or more machine learning models () trained to render a feature assessment of a medical image. The graphical user interface () provides a user interaction with the artificial intelligence engine () to manipulate a salient visualization of the feature assessment of the medical image by the machine learning model(s) ().

Salient visual explanations of feature assessments by machine learning models

According to various embodiments, a method of modifying an appearance of hair in an image of a head of a subject is disclosed. The method comprises providing, as an input to a first trained neural network model, an image of a head of a subject having a region of hair; generating, using the first trained neural network model and based on the image of the head, a hair layer comprising an estimated representation of portions of the image containing hair, and a face layer comprising an estimated representation of the head of the subject with the region of hair having been removed; providing, as an input to a second trained neural network model, an indication of a defined hairstyle to be incorporated into the image, and the generated hair layer; generating, using the second trained neural network model, a modified hair layer based on the indication of the defined hairstyle and the generated hair layer; and generating, using a processor, a modified image of the head of the subject by applying the modified hair layer to the generated face layer.

Modifying an appearance of hair

The invention discloses an apparatus for converting data and for assessing data. The apparatus comprises a processor. For converting data, the processor is configured to train a neural network arrangement to generate a first vector to represent input data, each element of the first vector representing a defined feature of the input data. For assessing data, the processor is configured to provide a neural network trained to generate a first vector representing input data; provide input data to the trained neural network; and generate, using the trained neural network, a first vector representing the input data, wherein each element of the generated first vector represents a defined feature of the input data. Methods and a computer program product are also disclosed.

Generating vectors from data

Described herein are systems and methods for training first and second neural network models. A system comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to set a weight in the second model based on a corresponding weight in the first model, train the second model on a first dataset, wherein the training comprises updating the weight in the second model and adjust the corresponding weight in the first model based on the updated weight in the second model.

Training first and second neural network models

The invention relates to a portable device () comprising a bottom surface (BS) intended to be in contact with a textile (TXT). The portable device comprises an image sensor () for taking an image of a textile, and an illumination system () for illuminating a portion of the textile when said image is being taken. The portable device also comprises a control unit () for executing an algorithm stored in the portable device, using the taken image as an input of said algorithm, to obtain a classification of the textile. The image sensor and the control unit are integrated within the portable device. The image sensor has an active surface sensitive to light, which is oriented compared to said bottom surface (BS), with an absolute value of the orientation angle being in the range 15-70 degrees; and/or the illumination system () has a light source oriented compared to the said bottom surface (BS), with an absolute value of the orientation angle being in the range 15-70 degrees.

Portable device with image sensor and illumination system for textile classification

A concept for training a neural network model. The concept comprises receiving training data and test data, each comprising a set of annotated images. A neural network model is trained using the training data with an initial regularization parameter. Loss functions of the neural network for both the training data and the test data are used to modify the regularization parameter, and the neural network model is retrained using the modified regularization parameter. This process is iteratively repeated until the loss functions both converge. A system, method and a computer program product embodying this concept are disclosed.

Training a neural network model

The invention provides a method for identifying a characteristic of one or more ultrasound images, wherein each image is of a subject imaged by an ultrasound probe using an ultrasound imaging process. The method includes obtaining a manipulation signal indicative of a manipulation of the ultrasound probe during the imaging process. A portion of the manipulation signal, which indicates the manipulation of the probe during a time period, is obtained. The obtained portion is associated with one or more ultrasound images. A neural network system is then used to classify a characteristic of the one or more ultrasound images based on both the obtained portion of the manipulation signal and the one or more images themselves. Such classification comprises applying one or more convolution kernels on the obtained portion of the manipulation signal to generate a convolution output representative of the obtained portion of the manipulation signal, and classifying the convolution output to indicate the characteristic of the one or more ultrasound images associated with the obtained portion.

System and method of identifying characteristics of ultrasound images

A portable textile treatment device includes a heatable soleplate for contacting and treating the textile, an image sensor and an illumination system. The image sensor has an active surface sensitive and takes an image of the textile, and the illumination system has a light source that illuminates a portion of the textile when the image is being taken. The portable device also includes a control unit configured to execute an algorithm stored in the portable device for obtaining a classification of the textile based on taken image, and based on the classification for controlling at least one operating parameter of the portable textile treatment device. The image sensor and/or light source are oriented with respect to the surface of the heatable soleplate, with an absolute value of an orientation angle being in the range from 15 to 70&#xb0; C.

Portable textile treatment device with image sensor and illumination system for textile classification

The invention relates to a method of operating a textile treatment device comprising a heatable soleplate intended to be in contact with a textile for treating the textile. The method comprises a first step () of setting a first temperature target for the heatable soleplate, and a step () of detecting movement of said textile treatment device. If the step () of 5 detecting movement did not detect any movement of said textile treatment device during more than a given first time duration, a step () of actively decreasing the temperature of the heatable soleplate () up to reaching a first given temperature having a value below said first temperature target.

Method for actively decreasing the temperature of a heatable soleplate in a textile treatment device

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Rosmalen, Netherlands

Binyam Gebrekidan Gebre