International Business Machines Corporation

Shafiqul Abedin

Hakan Bulu

David James Beymer

Tanveer Fathima Syeda-Mahmood

Mehdi Moradi

Yaniv Gur

Anup Pillai

Guy Talmor

Ehsan Dehghan Marvast

Hongzhi Wang

A candidate generator generates a set of candidate three-dimensional image patches from an input volume. A candidate classifier classifies the set of candidate three-dimensional image patches as containing or not containing disease. Classifying the set of candidate three-dimensional image patches comprises generating an attention mask for each given candidate three-dimensional image patch within the set of candidate three-dimensional image patches to form a set of attention masks, applying the set of attention masks to the set of candidate three-dimensional image patches to form a set of masked image patches, and classifying the set of masked image patches as containing or not containing the disease. The candidate classifier applies soft attention and hard attention to the three-dimensional image patches such that distinctive image regions are highlighted proportionally to their contribution to classification while completely removing image regions that may cause confusion.

Disease detection with masked attention

Annotation of large image datasets is provided. In various embodiments, a plurality of medical images is received. At least one collection is formed containing a subset of the plurality of medical images. One or more image from the at least one collection is provided to each of a plurality of remote users. An annotation template is provided to each of the plurality of remote users. Annotations for the one or more image are received from each of the plurality of remote users. The annotations and the plurality of medical images are stored together.

Rapid cross-validated ground truth annotation of large image datasets for image analytics

Web-based annotation of three dimensional medical imagery is provided. In various embodiments, a plurality of two dimensional medical images is read from a data store. The plurality of two dimensional images is a subset of a three dimensional medical imaging study. The plurality of two dimensional medical images is provided to a remote user. Regional annotations for each of the plurality of medical images are received from the remote user. A volumetric description of the three dimensional imaging study is generated by interpolation of the regional annotations. The volumetric description of the three dimensional imaging is provided for rendering and display to the remote user.

3D web-based annotation

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San Jose, CA, United States of America

Shafiqul Abedin