Koninklijke Philips Corporation N.v.

Yuan Ling

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Junyi Liu

Rithesh Sreenivasan

Vivek Varma Datla

Christine Menking Swisher

Cristhian Mauricio Potes Blandon

Jonathan Rubin

Tilak Raj Arora

Shreya Anand

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

Techniques are described herein for drawing conclusions using free form texts and external resources. In various embodiments, free form input data () may be segmented () into a plurality of input data segments. A first input data segment may be compared () with an external resource () to identify a first candidate conclusion. A reinforcement learning trained agent () may be applied () to make a first determination of whether to accept or reject the first candidate conclusion. Similar actions may be performed with a second input data segment to make a second determination of whether to accept or reject a second candidate conclusion. A final conclusion may be presented () based on the first and second determinations of the reinforcement learning trained agent with respect to at least the first candidate conclusion and the second candidate conclusion.

Drawing conclusions from free form texts with deep reinforcement learning

Techniques are provided for generating and applying a granular attention hierarchical neural network model to classify a document. In various embodiments, data indicative of the document may be obtained () and processed () into a first layer of two or more layers of a hierarchical network model using a dual granularity attention mechanism to generate first layer output data, wherein the dual granularity attention mechanism weighs some portions of the data indicative of the document more heavily. Some portions of the data indicative of the document are integrated into the hieratical network model during training of the dual granularity attention mechanism. The first layer output data may be processed () in the second of two or more layers of the hierarchical network model to generate second layer output data. A classification label can be generated () from the second layer output data.

Hierarchical neural networks with granularized attention

Various embodiments described herein relate to a method, system, and non-transitory machine-readable medium including one or more of the following: extracting a first concept from input data presented for processing by a downstream function; identifying external data from an external resource based on the first concept; extracting a second concept from the external data; revising the first concept based on the second concept to produce a revised concept, wherein revising includes: applying a machine learning agent to determine whether to keep the first concept or adopt the second concept, and adopting the second concept in place of the first concept for use as the revised concept based on a decision by the machine learning agent to adopt the second concept; and further processing the revised concept according to the downstream function to generate an output.

Extracted concept normalization using external evidence

A system, method and device for determining and notifying a clinician of information relevant to the clinician. The method that is performed by the device or system includes identifying at least one keyword in a user profile of a clinician, identifying at least one content word in a new information item, determining a relevance score between the new information item and the clinician based on the at least one keyword and the at least one content word and when the relevance score is above a predetermined threshold value, generating a notification for the clinician indicating the new information item.

Device, system, and method for determining information relevant to a clinician

A method for generating a textual description from a medical image, comprising: receiving a medical image having a first modality to a system configured to generate a textual description of the medical image; determining, using an imaging modality classification module, that the first modality is a specific one of a plurality of different modalities; determining, using an anatomy classification module, that the medical image comprises information about a specific portion of an anatomy; identifying, by an orchestrator module based at least on the determined first modality, which of a plurality of different text generation models to utilize to generate a textual description from the medical image; generating, by a text generation module utilizing the identified text generation model, a textual description from the medical image; and reporting, via a user interface of the system, the generated textual description.

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Somerville, MA, United States of America

Yuan Ling