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公开(公告)号：US20240233419A9
公开(公告)日：2024-07-11
申请号：US18128290
申请日：2023-03-30
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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公开(公告)号：US20250111517A1
公开(公告)日：2025-04-03
申请号：US18374473
申请日：2023-09-28
Inventor： Wenzhe Cui , Meng Zheng , Arun Innanje , Ziyan Wu , Terrence Chen
Abstract: An apparatus for annotating a medical image may be configured to obtain, automatically, an outline of a region of interest (ROI) in the medical image and determine, based on one or more inner control points and one or more outer control points. The one or more inner control points may be located within the ROI and the one or more outer control points may be located outside of the ROI. The outline may be subsequently adjusted based on a user input and the adjusted outline may be used to generate a segmentation of the ROI.
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公开(公告)号：US20250029720A1
公开(公告)日：2025-01-23
申请号：US18225009
申请日：2023-07-21
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Yikang Liu , Lin Zhao , Terrence Chen , Arun Innanje , Abhishek Sharma , Wenzhe Cui , Xiao Fan
Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.
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公开(公告)号：US20240135737A1
公开(公告)日：2024-04-25
申请号：US18128290
申请日：2023-03-29
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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