Koninklijke Philips Corporation N.v.

Jonathan Rubin

Bryan Conroy

Gajendra Jung Katuwal

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Christine Menking Swisher

Rithesh Sreenivasan

Cristhian Mauricio Potes Blandon

Yuan Ling

Saeed Babaeizadeh

Asif Rahman

Saman Parvaneh

Jack He

A method () for generating a textual description of a medical image, comprising: receiving () a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting () the anatomical region in the received medical image from a remainder of the image; identifying () at least one of the one or more abnormalities in the segmented anatomical region; extracting () one or more features from the identified abnormality; generating (), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (), via a user interface of the system, the generated textual description of the identified abnormality.

Convolutional localization networks for intelligent captioning of medical images

A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for the disease states based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.

Dynamic and locally-faithful explanation of machine learning models for precision medicine

A system and method for performing image processing. The method includes receiving an image of a first modality and a real image of a second modality, the image of the first modality and the image of the second modality capturing respective images of a same subject, applying a first trained model to the image of the first modality to generate an artificial image mimicking the image of the second modality, applying a second trained model to the artificial image mimicking the image of the second modality and data of the image of the first modality, and outputting at least one conclusion regarding the generated artificial image.

System and method for providing stroke lesion segmentation using conditional generative adversarial networks

A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.

Clinical case search and generation system and method based on a probabilistic encoder-generator framework

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor () to perform an atrial fibrillation (AF) detection method (). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) () to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device () to display the rhythm assigned to the ECG signal.

Detecting atrial fibrillation using short single-lead ECG recordings

A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.

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Cambridge, MA, United States of America

Jonathan Rubin