Patents by Inventor Yixuan Yuan
Yixuan Yuan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250117944Abstract: Methods and systems for pre-processing immunocytochemistry images for machine learning are provided. The pre-processing method includes receiving paired positive and negative multi-protein images and segmenting stains of the paired multi-protein images. The method also includes labelling each image pixel corresponding to a cell of the paired multi-protein images and translating image pixel information of the labelled image pixels to tabular form to generate a table of cell coordinates and geometrical characteristics for each of the paired multi-protein images. The method further includes generating two cell-paired tables for each of the paired multi-protein images based on Euclidean distance-based pairing prior to input for the machine learning, where the Euclidean distance-based pairing is based on the cell coordinates in the table of cell coordinates and geometrical characteristics for each of the paired multi-protein images.Type: ApplicationFiled: October 4, 2023Publication date: April 10, 2025Inventors: Irfan AHMED, Condon LAU, Fraser HILL, Wei ZHANG, Xinyue LI, Duncan HOCKLEY, Wah CHEUK, Tin Yan Elaine CHEUNG, Chi Shing CHO, Wing Lun LAW, Antonio GIULIANO, Daniela HERNANDEZ MUGUIRO, Yixuan YUAN, Cheuk Ying Cherry CHAN, Wing Sze KEE
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Patent number: 12150400Abstract: A method for performing a crop yield estimation using a semi-supervised deep convolution neural network is provided. The method includes receiving monitoring data from a drone, wherein the monitoring data comprises a video of the crops captured by the drone; sampling the video by a predefined frame rate to obtain one or more images; inputting the images to a crop yield estimation model to obtain one or more result data, wherein the crop yield estimation model comprises a generator and a discriminator each comprising one or more DCNNs, and wherein the crop yield estimation model is trained by a semi-supervised learning method; and performing a quantity estimation and a quality estimation corresponding to the crops as shown in the images according to the one or more result data, so as to determine a total number and maturities of the crops respectively.Type: GrantFiled: December 2, 2021Date of Patent: November 26, 2024Assignee: City University of Hong KongInventors: Yixuan Yuan, Xiaochun Mai
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Publication number: 20240348341Abstract: An integrated MWP processing engine based on a thin-film lithium niobate (LN) platform capable of performing computation tasks of analog signals up to 92G samples per second is provided. By integrating a high-speed electro-optic modulation block and a multi-purpose low-loss signal processing section on the same chip fabricated from a 4-inch wafer-scale process, we demonstrate high-speed analog computation, i.e. first- and second-order temporal integration and differentiation with computing accuracies up to 98.1%, and deploy these functions to showcase three proof-of-concept applications, namely, ordinary differential equation solving, ultra-wideband signal generation and high-speed edge detection of images. We further leverage the image edge detector to enable a photonic-assisted image segmentation model that effectively outlines boundaries of melanoma lesion in medical images, achieving orders of magnitude faster processing speed and lower power consumption than conventional electronic processors.Type: ApplicationFiled: August 3, 2023Publication date: October 17, 2024Inventors: Hanke FENG, Cheng WANG, Tong GE, Xiaoqing GUO, Yixuan YUAN
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Patent number: 12020436Abstract: The present invention provides an unsupervised domain adaptive segmentation network comprises a feature extractor configured for extracting features from a 3D MRI scan image; a decorrelation and whitening module configured for preforming decorrelation and whitening transformation on the extracted features to obtain whitened features; a domain-specific feature translation module configured for translating domain-specific features from a source domain into a target domain for adapting the unsupervised domain adaptive network to the target domain; and a classifier configured for projecting the whitened features into a zonal segmentation prediction. By implementing the domain-specific feature translation module for transferring the knowledge learned from the labeled source domain data to unlabeled target domain data, domain gap between the source and target data can be narrowed.Type: GrantFiled: December 6, 2021Date of Patent: June 25, 2024Assignee: City University of Hong KongInventors: Yixuan Yuan, Xiaoqing Guo
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Patent number: 11995810Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.Type: GrantFiled: April 27, 2021Date of Patent: May 28, 2024Assignee: City University of Hong KongInventors: Condon Lau, Tik Ho Hui, Yixuan Yuan, Zhen Chen, Chi Shing Cho, Wah Cheuk, Wing Lun Law, Mohamad Ali Marashli, Anupam Pani, Fraser Hill
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Patent number: 11963788Abstract: The present invention provides a graph-based prostate diagnosis network (GPD-Net) and a method for using the same to predict a prostate health status of a patient from a 3D magnetic resonance imaging (MRI) scan containing a plurality of 2D MRI slices. The GPD-Net only demands patient-level annotations of MRI scan for training by formulating the diagnosis task of 3D prostate MRI scan in a multi-instance learning (MIL) strategy, and regarding each 2D MRI slice in the 3D prostate MRI scan as an instance. The GPD-Net includes a plurality of importance-guided graph convolutional layers to explore the diagnostic information with the importance-based topology. The present invention provides accurate prediction of prostate diseases and achieve more reliable diagnosis from MRI scans, therefore can effectively alleviate the workload of clinician in viewing the slices of MRI scan.Type: GrantFiled: December 17, 2021Date of Patent: April 23, 2024Assignee: City University of Hong KongInventors: Yixuan Yuan, Zhen Chen
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Patent number: 11961618Abstract: The present invention provides a task interaction network which can jointly perform, based on multi parametric-magnetic resonance imaging scan images, a segmentation task to locate prostate cancer areas and a classification task to access aggressiveness of lesions. The task interaction network comprises a backbone network, an auxiliary segmentation branch, a classification branch having a lesion awareness module, and a main segmentation branch having a category allocation module. The auxiliary segmentation branch is utilized to predict an initial lesion mask as location guidance information for the classification branch to perform the classification task. The lesion awareness module is configured to refine the initial lesion mask to make it more accurate. Moreover, weights used in classification branch can serve as the category prototypes for generating category guidance features via the category allocation module to assist the main segmentation branch to perform the segmentation task.Type: GrantFiled: November 17, 2021Date of Patent: April 16, 2024Assignee: City University of Hong KongInventors: Yixuan Yuan, Meilu Zhu
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Publication number: 20230190179Abstract: The present invention provides a graph-based prostate diagnosis network (GPD-Net) and a method for using the same to predict a prostate health status of a patient from a 3D magnetic resonance imaging (MRI) scan containing a plurality of 2D MRI slices. The GPD-Net only demands patient-level annotations of MRI scan for training by formulating the diagnosis task of 3D prostate MRI scan in a multi-instance learning (MIL) strategy, and regarding each 2D MRI slice in the 3D prostate MRI scan as an instance. The GPD-Net includes a plurality of importance-guided graph convolutional layers to explore the diagnostic information with the importance-based topology. The present invention provides accurate prediction of prostate diseases and achieve more reliable diagnosis from MRI scans, therefore can effectively alleviate the workload of clinician in viewing the slices of MRI scan.Type: ApplicationFiled: December 17, 2021Publication date: June 22, 2023Inventors: Yixuan YUAN, Zhen CHEN
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Publication number: 20230177692Abstract: The present invention provides an unsupervised domain adaptive segmentation network comprises a feature extractor configured for extracting features from a 3D MRI scan image; a decorrelation and whitening module configured for preforming decorrelation and whitening transformation on the extracted features to obtain whitened features; a domain-specific feature translation module configured for translating domain-specific features from a source domain into a target domain for adapting the unsupervised domain adaptive network to the target domain; and a classifier configured for projecting the whitened features into a zonal segmentation prediction. By implementing the domain-specific feature translation module for transferring the knowledge learned from the labeled source domain data to unlabeled target domain data, domain gap between the source and target data can be narrowed.Type: ApplicationFiled: December 6, 2021Publication date: June 8, 2023Inventors: Yixuan YUAN, Xiaoqing GUO
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Publication number: 20230172091Abstract: A method for performing a crop yield estimation using a semi-supervised deep convolution neural network is provided. The method includes receiving monitoring data from a drone, wherein the monitoring data comprises a video of the crops captured by the drone; sampling the video by a predefined frame rate to obtain one or more images; inputting the images to a crop yield estimation model to obtain one or more result data, wherein the crop yield estimation model comprises a generator and a discriminator each comprising one or more DCNNs, and wherein the crop yield estimation model is trained by a semi-supervised learning method; and performing a quantity estimation and a quality estimation corresponding to the crops as shown in the images according to the one or more result data, so as to determine a total number and maturities of the crops respectively.Type: ApplicationFiled: December 2, 2021Publication date: June 8, 2023Inventors: Yixuan YUAN, Xiaochun MAI
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Publication number: 20230154610Abstract: The present invention provides a task interaction network which can jointly perform, based on multi parametric-magnetic resonance imaging scan images, a segmentation task to locate prostate cancer areas and a classification task to access aggressiveness of lesions. The task interaction network comprises a backbone network, an auxiliary segmentation branch, a classification branch having a lesion awareness module, and a main segmentation branch having a category allocation module. The auxiliary segmentation branch is utilized to predict an initial lesion mask as location guidance information for the classification branch to perform the classification task. The lesion awareness module is configured to refine the initial lesion mask to make it more accurate. Moreover, weights used in classification branch can serve as the category prototypes for generating category guidance features via the category allocation module to assist the main segmentation branch to perform the segmentation task.Type: ApplicationFiled: November 17, 2021Publication date: May 18, 2023Inventors: Yixuan YUAN, Meilu ZHU
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Publication number: 20220343473Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.Type: ApplicationFiled: April 27, 2021Publication date: October 27, 2022Inventors: Condon Lau, Tik Ho Hui, Yixuan Yuan, Zhen Chen, Chi Shing Cho, Wah Cheuk, Wing Lun Law, Mohamad Ali Marashli, Anupam Pani, Fraser Hill
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Patent number: 11238583Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.Type: GrantFiled: March 25, 2020Date of Patent: February 1, 2022Assignee: City University of Hong KongInventors: Condon Lau, Yixuan Yuan, Chi Shing Cho, Wah Cheuk, Wan San Victor Ma, Wing Lun Law
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Publication number: 20210304401Abstract: A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.Type: ApplicationFiled: March 25, 2020Publication date: September 30, 2021Inventors: Condon Lau, Yixuan Yuan, Chi Shing Cho, Wah Cheuk, Wan San Victor Ma, Wing Lun Law