Koninklijke Philips Corporation N.v.

Luyin Zhao

Lilla Boroczky

Lalitha Agnihotri

Kwok Pun Lee

Michael Chun-Chieh Lee

Michael Chun-chieh Lee

Charles Andrew Powell

Alain C Borczuk

Steven Kawut

Michael C Lee

Lalitha Agnihorti

Lalitha A Agnihotri

Michael CC Lee

Alain Borczuk

Charles Powell

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/or 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.

Method and apparatus for refining similar case search

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.

Advanced computer-aided diagnosis of lung nodules

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.

Method and system for cross-modality case-based computer-aided diagnosis

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.

Method and device for case-based decision support

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.

Methods for feature selection using classifier ensemble based genetic algorithms

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.

Methods and apparatus to integrate systematic data scaling into genetic algorithm-based feature subset selection

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).

System and method for automated detection and segmentation of tumor boundaries within medical imaging data

Optimizing example-based computer-aided diagnosis (CADx) is accomplished by clustering volumes-of-interest (VOIs) () in a database () into respective clusters according to subjective assessment of similarity (S). An optimal set of volume-of-interest (VOI) features is then selected for fetching examples such that objective assessment of similarity, based on the selected features, clusters, in a feature space, the database VOIs so as to conform to the subjectively-based clustering (S). The fetched examples are displayed alongside the VOI to be diagnosed for comparison by the clinician. Preferably, the displayed example is user-selectable for further display of prognosis, therapy information, follow up information, current status, and/or clinical information retrieved from an electronic medical record (S).

Clinician-driven example-based computer-aided diagnosis

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.

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Lawrenceville, NJ, United States of America

Luyin Zhao