Abstract: The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.

Abstract: A system and a method for Alzheimer's disease prediction using a neural network, a computer-readable recording medium with a stored program, and a computer program product are provided. The processor obtains a first brain MRI data, a second brain MRI data, a first neuropsychological assessment score, and a second neuropsychological assessment score. The processor obtains a plurality of image feature data according to the first brain MRI data and the second brain MRI data. Each image feature data is selected from a group consisting of a plurality of hippocampal subfield geometric change data. The processor obtains a neuropsychological change data according to the first neuropsychological assessment score and the second neuropsychological assessment score. The processor obtains an Alzheimer's disease prediction result according to the neural network module, the image feature data, and the neuropsychological change data.

Abstract: The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.

Abstract: A convolutional neural network (CNN) and associated method for identifying basal cell carcinoma are disclosed. The CNN comprises two convolution layers, two pooling layers and at least one fully-connected layer. The first convolution layer uses initial Gabor filters that model the kernel parameters setting in advance based on human professional knowledge. The method uses collagen fiber images for training images and converts doctors' knowledge to initiate the Gabor filters as featuring computerization. The invention provides better training performance in terms of training time consumption and training material overhead.

Abstract: A system and a method for Alzheimer's disease prediction using a neural network, a computer-readable recording medium with a stored program, and a computer program product are provided. The processor obtains a first brain MRI data, a second brain MRI data, a first neuropsychological assessment score, and a second neuropsychological assessment score. The processor obtains a plurality of image feature data according to the first brain MRI data and the second brain MRI data. Each image feature data is selected from a group consisting of a plurality of hippocampal subfield geometric change data. The processor obtains a neuropsychological change data according to the first neuropsychological assessment score and the second neuropsychological assessment score. The processor obtains an Alzheimer's disease prediction result according to the neural network module, the image feature data, and the neuropsychological change data.

Abstract: The present invention relates to computing-implemented method and system that improves matrix multiplication efficiency, especially to method and system optimizing matrix multiplication using sparse basis approach. Matrices to be multiplied are organized into specially ordered vectors with zero values, facilitates speed up during linear combination computation or synthesis process.

Abstract: A data compression method, a data compression system and an operation method of a deep learning acceleration chip are provided. The data compression method includes the following steps. A filter coefficient tensor matrix of a deep learning model is obtained. A matrix decomposition procedure is performed according to the filter coefficient tensor matrix to obtain a sparse tensor matrix and a transformation matrix, which is an orthonormal matrix. The product of the transformation matrix and the filter coefficient tensor matrix is the sparse tensor matrix. The sparse tensor matrix is compressed. The sparse tensor matrix and the transformation matrix, or the sparse tensor matrix and a restoration matrix, are stored in a memory. A convolution operation result is obtained by the deep learning acceleration chip using the sparse tensor matrix. The convolution operation result is restored by the deep learning acceleration chip using the restoration matrix.

Abstract: The present invention relates to computing-implemented method and system that improves matrix multiplication efficiency, especially to method and system optimizing matrix multiplication using sparse basis approach. Matrices to be multiplied are organized into specially ordered vectors with zero values, facilitates speed up during linear combination computation or synthesis process.

Abstract: The present disclosure is to provide a computer-aided cell segmentation method for determining cellular Nuclear-to-Cytoplasmic ratio, which comprises acts of obtaining a cytological image using non-invasive in vivo biopsy technique; performing a nuclei segmentation process to identify a position and a contour of each of identified nuclei in the cytological image; performing a cytoplasmic process with an improved active contour model to obtain a cytoplasmic region for each identified nucleus based; and determine a cellular Nuclear-to-Cytoplasmic ratio based on the obtained nucleus and cytoplasmic regions.

Abstract: The present disclosure relates to a method for providing biomarker for early detection of Alzheimer's Disease (AD), and particularly to a method that is able to enhance the accuracy of predicting AD from Mild Cognitive Impairment (MCI) patients using the Hippocampus magnetic resonance imaging (MRI) scans and Mini-Mental State Examination (MMSE) data. The providing MRI images containing the anatomical structure of Hippocampus biomarker and MMSE data as a training data set; training a processor using the training data set, and the training comprising acts of receiving MRI images and MMSE data as a testing data set from a target; and classifying the test data by the trained processor to include aggregating predictions.

Abstract: The present disclosure is to provide a computer-aided cell segmentation method for determining cellular Nuclear-to-Cytoplasmic ratio, which comprises acts of obtaining a cytological image using non-invasive in vivo biopsy technique; performing a nuclei segmentation process to identify a position and a contour of each of identified nuclei in the cytological image; performing a cytoplasmic process with an improved active contour model to obtain a cytoplasmic region for each identified nucleus based; and determine a cellular Nuclear-to-Cytoplasmic ratio based on the obtained nucleus and cytoplasmic regions.

Abstract: A convolutional neural network (CNN) and associated method for identifying basal cell carcinoma are disclosed. The CNN comprises two convolution layers, two pooling layers and at least one fully-connected layer. The first convolution layer uses initial Gabor filters that model the kernel parameters setting in advance based on human professional knowledge. The method uses collagen fiber images for training images and converts doctors' knowledge to initiate the Gabor filters as featuring computerization. The invention provides better training performance in terms of training time consumption and training material overhead.

Abstract: The image data analytics for computation configuration method and computer system with computation accessibility is provided. The computation accessibility and configuration on image data processing are implemented by a plurality of computers having a plurality of processors and a plurality of data storages, the image data analytics for computation accessibility and configuration includes the following steps: inputting an original image by an input device and initializing the original image; defining a plurality of tiles equally dividing the original image into a same size of a regular shape; transferring the plurality of image regions to form a graph having vertices and edges; cutting the graph into a plurality of sub-graphs; arranging the plurality of sub-graphs to the plurality of processors or cores to conduct parallel processing simultaneously for analyzing the image data; and storing a plurality of processing results respectively in the plurality of data storages.

Abstract: The image data analytics for computation configuration method and computer system with computation accessibility is provided. The computation accessibility and configuration on image data processing are implemented by a plurality of computers having a plurality of processors and a plurality of data storages, the image data analytics for computation accessibility and configuration includes the following steps: inputting an original image by an input device and initializing the original image; defining a plurality of tiles equally dividing the original image into a same size of a regular shape; transferring the plurality of image regions to form a graph having vertices and edges; cutting the graph into a plurality of sub-graphs; arranging the plurality of sub-graphs to the plurality of processors or cores to conduct parallel processing simultaneously for analyzing the image data; and storing a plurality of processing results respectively in the plurality of data storages.

Abstract: An electrocardiography signal extraction method includes receiving an electrocardiography signal, detecting a peak of a waveform of the electrocardiography signal, separating the waveform into left and right waves, normalizing the left wave and a plurality of scales of Gaussian function, comparing the normalized left wave with a left part of the normalized scales of Gaussian function, acquiring a left part error function, indicating a left minimum comparative error, selecting a left scale of Gaussian function with the left minimum comparative error, obtaining a left duration of the waveform, normalizing the right wave, comparing the normalized right wave with a right part of the normalized scales of Gaussian function, acquiring a right part error function, indicating a right minimum comparative error, selecting a right scale of Gaussian function with the right minimum comparative error, obtaining a right duration of the waveform, and obtaining an extracted wave.

Abstract: An electrocardiography signal extraction method is performed on a processor of a computer system and includes receiving an electrocardiography signal, performing a time-frequency transformation on the received electrocardiography signal to generate a corresponding scalogram, selecting a predetermined R-pertinent scale, performing the time-frequency transformation at the selected predetermined R-pertinent scale to generate a R-pertinent summarized response, obtaining a R peak position, selecting a predetermined QRS-pertinent scale, performing the time-frequency transformation at the selected predetermined QRS-pertinent scale, obtaining a Q peak position and a S peak position of the electrocardiography signal by finding relative maximum negative responses before and behind the R peak position respectively, obtaining a QRSon position and a QRSoff position by finding relative minimum second derivatives of the responses before the Q peak position and behind the S peak position, respectively.

Abstract: A cell image segmentation method includes receiving a cell image, performing a nuclei initialization step to find an internal marker and an external marker to obtain a potential nuclei and a potential cell boundary, calculating a gradient map of the received cell image, performing a filtering step on the gradient map to generate a filtered gradient map, performing a nuclei detection step to obtain a segmented nuclei, and performing a nuclei validation step to obtain a valid nuclei. The nuclei initialization step includes performing a blob detection step to obtain a nuclei candidate, an outlier removal step to obtain the internal marker, a distance transform step to obtain a distance map, and a cell boundary initialization step to obtain the external marker. In another embodiment, a nuclear-to-cytoplasmic ratio evaluation method using the above cell image segmentation method is proposed.

Abstract: The quantifying method for intrinsic data transfer rate of algorithms is provided. The provided quantifying method for an intrinsic data transfer rate includes steps of: detecting whether or not a datum is used; providing a dataflow graph G including n vertices and m edges, and a Laplacian matrix L having ixj elements L(i,j) when the datum is not reused, wherein each of the vertices represents one of an operation and a datum, each of the edges represents a data transfer, and vi is the ith vertex; and using the Laplacian matrix L to estimate a maximum quantity of the intrinsic data transfer rate.

Abstract: This invention discloses a method for object tracking, including determination of an area scaling ratio of the object in a video image sequence. In one embodiment, a centroid of the object is determined. One or more directed straight lines are selected, each passing through the centroid, extending from an end of the object's boundary to an opposite end thereof, and having a direction that is upward. A length scaling ratio for each directed straight line is determined by: determining a motion vector for each selected pixel on the line; computing a scalar component of the motion vector projected onto the line; estimating a change of the line's length according to the scalar components obtained for all pixels; and determining the length scaling ratio according to the change of the line's length. The area scaling ratio is computed based on the length scaling ratios for all directed straight lines.

Abstract: A cell image segmentation method includes receiving a cell image, performing a nuclei initialization step to find an internal marker and an external marker to obtain a potential nuclei and a potential cell boundary, calculating a gradient map of the received cell image, performing a filtering step on the gradient map to generate a filtered gradient map, performing a nuclei detection step to obtain a segmented nuclei, and performing a nuclei validation step to obtain a valid nuclei. The nuclei initialization step includes performing a blob detection step to obtain a nuclei candidate, an outlier removal step to obtain the internal marker, a distance transform step to obtain a distance map, and a cell boundary initialization step to obtain the external marker. In another embodiment, a nuclear-to-cytoplasmic ratio evaluation method using the above cell image segmentation method is proposed.