Tencent America LLC

Zhen Qian

Wei Fan

Chao Huang

Hui Tang

Lianyi Han

Kun Wang

Yusheng Xie

Min Tu

Shih-Yao Lin

Tao Yang

Zhimin Huo

Yaliang Li

Xiaozhong Chen

Shihyao Lin

Yufan Xue

There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform obtaining video data including at least one body part of a person, selecting keypoints of the at least one body part based on a predetermined rehabilitation category, extracting a motion feature of the at least one body part from the video data, scoring the motion feature based on the predetermined rehabilitation category, and generating a display illustrating the motion feature and said scoring of the motion feature.

Vision-based motion capture system for rehabilitation training

A method and device for generating a three dimensional (3D) bounding box of a region of interest (ROI) of a patient include receiving a two dimensional (2D) maximum intensity projection (MIP) image that is an axial view of the patient and a 2D MIP image that is a sagittal view of the patient. A first 2D bounding box of the ROI of the patient and a second 2D bounding box of the ROI of the patient are detected using the 2D MIP images. A 3D MIP image of the patient is received, and the 3D bounding box of the ROI of the patient is generated using the 3D MIP image, the first 2D bounding box, and the second 2D bounding box. The 3D MIP image including the 3D bounding box is provided.

System and method for identifying target regions prior to organs at risk segmentation

A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.

Two and a half dimensional convolutional neural network for predicting hematoma expansion in non-contrast head computerized tomography images

A computer-implemented method of training a neural network for organ segmentation may be provided. The method may include: collecting a set of digital sample images from a database; inputting the collected set of digital images into a neural network recognition model; and training the neural network recognition model to recognize a first object in a first digital image as a specific object based on the first object being similar to a second object in a second digital image. The method may comprise predicting a signed distance map (SDM) in conjunction with a segmentation map.

Shape-aware organ segmentation by predicting signed distance maps

A method and apparatus include receiving a first sentence including a first set of words, and a second sentence including a second set of words. A first set of vectors corresponding to the first set of words of the first sentence, and a second set of vectors corresponding to the second set of words of the second sentence are generated using a word embedding model. A similarity matrix based on the first set of vectors and the second set of vectors is generated. An alignment score associated with the first set of vectors and the second set of vectors is determined using the similarity matrix. The alignment score is transmitted to permit information retrieval based on a similarity between the first sentence and the second sentence.

Proximity information retrieval boost method for medical knowledge question answering systems

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Zhen Qian