Luyin Zhao
Luyin Zhao, Lawrenceville, NJ US
Patent application number | Description | Published |
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20100272338 | METHOD AND SYSTEM FOR CROSS-MODALITY CASE-BASED COMPUTER-AIDED DIAGNOSIS - A system and method for cross-modality case-based computer-aided diagnosis comprises storing a plurality of cases, each case including at least one image of one of a plurality of modalities and non-image information, mapping a feature relationship between a feature from images of a first modality to a feature from images of a second modality, and storing the relationship. | 10-28-2010 |
20100281037 | METHOD AND DEVICE FOR CASE-BASED DECISION SUPPORT - This invention relates to a method and device for case-based decision support. It proposes that a case-based decision support system is trained on inputs from several radiologists in order to have a “baseline” system, and then the system provides an option to a radiologist to refine the baseline system based on his/her inputs which either refine weights of features for similarity distance computation directly or provide new similarity ground truth clusters. By enabling modifying the similarity distance computation based on user inputs, this invention adapts similarity ground truth to different users with different experience and/or different opinions. | 11-04-2010 |
20110022622 | METHOD AND APPARATUS FOR REFINING SIMILAR CASE SEARCH - The invention relates to search for cases in a database. According to the proposed method and apparatus, similarity matching is performed between an input case and a set of cases in an initial search to receive similar cases by—using a given matching criterion. Then statistics on image and non-image-based features associated with the similar cases are calculated and presented to the user with the similar cases. In a search refinement the similar cases are refined by additional features that are determined by the user based on the statistics. The search refinement can be iterative depending on the user's need. | 01-27-2011 |
Luyin Zhao, Belleville, NY US
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20100036782 | METHODS FOR FEATURE SELECTION USING CLASSIFIER ENSEMBLE BASED GENETIC ALGORITHMS - Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed. | 02-11-2010 |
20100177943 | METHODS AND APPARATUS TO INTEGRATE SYSTEMATIC DATA SCALING INTO GENETIC ALGORITHM-BASED FEATURE SUBSET SELECTION - Methods and apparatus for training a system for developing a process of data mining, false positive reduction, computer-aided detection, computer-aided diagnosis and artificial intelligence are provided. A method includes choosing a training set from a set of training cases using systematic data scaling and creating a classifier based on the training set using a classification method. The classifier yields fewer false positives. The method is suitable for use with a variety of data mining techniques including support vector machines, neural networks and decision trees. | 07-15-2010 |
20110142301 | ADVANCED COMPUTER-AIDED DIAGNOSIS OF LUNG NODULES - Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms. | 06-16-2011 |
Luyin Zhao, White Plains, NY US
Patent application number | Description | Published |
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20090052768 | IDENTIFYING A SET OF IMAGE CHARACTERISTICS FOR ASSESSING SIMILARITY OF IMAGES - The invention relates to a method ( | 02-26-2009 |
20090138432 | CLINICIAN-DRIVEN EXAMPLE-BASED COMPUTER-AIDED DIAGNOSIS - Optimizing example-based computer-aided diagnosis (CADx) is accomplished by clustering volumes-of-interest (VOIs) ( | 05-28-2009 |
20090148007 | SYSTEM AND METHOD FOR AUTOMATED DETECTION AND SEGMENTATION OF TUMOR BOUNDARIES WITHIN MEDICAL IMAGING DATA - A method for segmenting regions within a medical image includes evaluating a set of candidate segmentations generated from an initial segmentation. Based on distance calculations for each candidate using derivative segmentations, the best candidate is recommended to clinician if it is better than the initial segmentation. This recommender realizes a most stable segmentation that will benefit follow-up computer aided diagnosis (i.e. classifying lesion to benign/malignant). | 06-11-2009 |
20090148011 | IN-SITU DATA COLLECTION ARCHITECTURE FOR COMPUTER-AIDED DIAGNOSIS - Automated diagnostic decision support ( | 06-11-2009 |
20090175514 | STRATIFICATION METHOD FOR OVERCOMING UNBALANCED CASE NUMBERS IN COMPUTER-AIDED LUNG NODULE FALSE POSITIVE REDUCTION - A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data. The method includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, wherein a data stratification method is used to balance the number of cases in different classes. The subset determined by GA is used to train the support vector machine to classify candidate region/volumes found within non-training data. | 07-09-2009 |
20090175531 | SYSTEM AND METHOD FOR FALSE POSITIVE REDUCTION IN COMPUTER-AIDED DETECTION (CAD) USING A SUPPORT VECTOR MACNINE (SVM) - A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-processing machine learning to maximize specificity and sensitivity of the classification to realize a reduction in number of false positive detections reported. The method includes training a classifier on a set of medical image training data selected to include a number of true and false regions, wherein the true and false regions are identified by a CAD process, and automatically segmented, wherein the segmented training regions are reviewed by at least one specialist to classify each training region for its ground truth, i.e., true or false, essentially qualifying the automatic segmentation, wherein a feature pool is identified and extracted from each segmented region, and wherein the pool of features is processed by genetic algorithm to identify an optimal feature subset, which subset is used to train a support vector machine, detecting, within non-training medical image data, regions that are candidates for classification, segmenting the candidate regions, extracting a set of features from each segmented candidate regions and classifying the candidate region using the support vector machine after training in accordance with the optimal feature subset, and processing the set of candidate features. | 07-09-2009 |
Luyin Zhao, White Plaiins, NY US
Patent application number | Description | Published |
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20090148010 | FALSE POSITIVE REDUCTION IN COMPUTER-ASSISTED DETECTION (CAD) WITH NEW 3D FEATURES - A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data. | 06-11-2009 |