International Business Machines Corporation

Noel C Codella

John Richard Smith

Nalini Kanta Ratha

Apostol Ivanov Natsev

Sharathchandra UmapathiRao Pankanti

Jonathan Hudson Connell, Ii

Jonathan H Connell

Parijat Dube

Bishwaranjan Bhattacharjee

Michael Robert Glass

Patrick D Watson

Matthew Leon Hill

John Ronald Kender

Hui Wu

Vince Siu

Gang Hua

Siyu Huo

Brian Michael Belgodere

Hariklia Deligianni

Tanveer Fathima Syeda-Mahmood

Mehdi Moradi

Sharathchandra Umapathirao Pankanti

Chung-Ching Lin

Christopher Frank Codella

James V Codella

Gong Leiguang

A drug delivery device includes a capsule, a logic circuit disposed within the capsule, and an actuator connected to the logic circuit and configured to expose an interior of the capsule to an exterior of the capsule upon activation by the logic circuit.

Drug delivery device having controlled delivery and confirmation

An apparatus includes a substrate, an array of channels disposed in the substrate, wherein first ends of the channels are exposed to an outside the apparatus, a material disposed in the channel that promotes growth of neural tissue, a plurality of electrodes disposed at second ends of the channels, wherein each channel is aligned with a respective one of the electrodes, and a chip electrically connected to the electrodes.

Biological neuron to electronic computer interface

Whether to train a new neural network model can be determined based on similarity estimates between a sample data set and a plurality of source data sets associated with a plurality of prior-trained neural network models. A cluster among the plurality of prior-trained neural network models can be determined. A set of training data based on the cluster can be determined. The new neural network model can be trained based on the set of training data.

Training transfer-focused models for deep learning

A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.

Generating and augmenting transfer learning datasets with pseudo-labeled images

Product recommendations are provided to a target user that take into account the style, interests, and hobbies of the target user and a desire by the target user to be unique from the target user's social group. In some aspects, a list of recommended products may be generated for the target user based on data about the target user's purchasing habits, social media interactions, or any other data. A list of products associated with users in the target user's social group may also be generated, for example, based on products purchased, currently worn by, or previously worn by the users in the target user's social group. A uniqueness-aware list of recommended products may then be generated from the list of recommended products by removing any products found in both the list of recommended products and the list of products associated with users in the target user's social group.

Determination of unique items based on generating descriptive vectors of users

Embodiments include methods, systems, and computer program products for treating skin lesions. Aspects include receiving an indication that a patient is oriented. Aspects also include acquiring data concerning an area of the patient, the area including a skin lesion. Aspects also include analyzing the data to distinguish between an affected region and an unaffected region of the area of the patient. Aspects also include excising the affected area.

Surgical skin lesion removal

A method of displaying information relating to an allergen present in a food substance together with a user profile, the method including capturing an image of a scene, segmenting the image to determining at least a segmentation of the food substance in the image, determining a classification of the food substance using the segmentation, determining a presence of the allergen in the food substance using the classification of the food substance and a database of allergens and food substances, determining a risk to a user using the user profile specifying a user sensitivity to the allergen, and displaying an output including a view of the user profile and the image, wherein the image is augmented to identify the presence of the allergen.

Automatic identification of food substance

Embodiments include method, systems and computer program products for reducing surface reflectance, for example in a photograph. Aspects include receiving an image set containing a plurality of images of an object. Aspects also include determining geometric transformations for the images and constructing a panorama of the object from the images using the geometric transformations. Aspects also include replacing a portion in the panorama with a corresponding replacement portion from the image set.

Surface reflectance reduction in images using non-specular portion replacement

Methods, systems, and computer program products for static image segmentation are provided herein. A method includes segmenting an image containing a target object into multiple regions; analyzing video content containing the target object to determine a similarity metric across the multiple segmented regions based on information associated with the multiple segmented regions; and applying the similarity metric to the image to identify two or more of the multiple segmented regions as being portions of the target object.

Static image segmentation

Techniques for detecting an event via social media content. A method includes obtaining multiple images from at least one social media source; extracting at least one visual semantic concept from the multiple images; differentiating an event semantic concept signal from a background semantic concept signal to detect an event in the multiple images; retrieving one or more images associated with the event semantic concept signal; grouping the one or more images associated with the event semantic concept signal; annotating the group of one or more images with user feedback; and displaying the annotated group of one or more images as a visual description of the detected event.

Social media event detection and content-based retrieval

A solution is presented for cardiac CT viewpoint recognition to identify the desired images for a specific view and subsequent processing and anatomy recognition. A new set of features is presented to describe the global binary pattern of cardiac CT images characterized by the highly attenuating components of the anatomy in the image. Five classic image texture and edge feature sets are used to devise a classification approach based on SVM classification, class likelihood estimation, and majority voting, to classify 2D cardiac CT images into one of six viewpoint categories that include axial, sagittal, coronal, two chamber, four chamber, and short axis views. Such an approach results in an accuracy of 99.4% in correct labeling of the viewpoints.

Viewpoint recognition in computer tomography images

A method, system and computer program for categorizing heart diseases is presented. An example method includes receiving a series of cardiac images of a heart, the cardiac images including a myocardium, and interior fibrous muscles of the heart. Cardiac images are segmented, into a myocardium segmentation showing an anatomical shape and a motion of the myocardium, and an interior fibrous muscles segmentation showing an anatomical shape and a motion of the interior fibrous muscles. The myocardium segmentation is converted into a regional characterization of the anatomical shape and motion of the myocardium. The interior fibrous muscles segmentation is converted to a regional characterization of the anatomical shape and motion of the interior fibrous muscles. Heart conditions are characterized based on the regional characterizations of the anatomical shape and the motion of the myocardium and the interior fibrous muscles.

Method and system for categorizing heart disease states

Methods, systems, and computer program products for static image segmentation are provided herein. A method includes segmenting a static image containing a target object into multiple regions based on one or more visual features of the static image; analyzing video content containing the target object to determine a similarity metric across the multiple segmented regions based on motion information associated with each of the multiple segmented regions; and applying the similarity metric to the static image to identify two or more of the multiple segmented regions as being portions of the target object.

Systems and articles of manufacture for image segmentation are provided herein, and include creating an anatomical model from training data comprising one or more imaging modalities, generating one or more simulated images in a target modality based on the anatomical model and one or more principles of physics pertaining to image contrast generation, and comparing the one or more simulated images to an unlabeled input image of a given imaging modality to determine a simulated image of the one or more simulated images to represent the unlabeled input image.

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White Plains, NY, United States of America

Noel C Codella