Abstract: A method for producing aluminum alloy materials suitable for use in the food industry includes processing of a liquid metal mixture having strontium in addition to aluminum by a twin roll continuous casting technique.

Abstract: The invention relates to a measurement method enabling on-line thickness measurement of the oxide layer formed on aluminum foil by FTIR spectrometer at low cost and precise manner, during aluminum-containing material production.

Abstract: 1.

Abstract: A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability P = ? i = 1 N ? p i y i ? ( 1 - p i ) 1 - y i , wherein N is a number of bags, pi is a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yi is a label where a value of 1 indicates malignancy and 0 otherwise.

Abstract: A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a “bag”, rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.

Abstract: A method for training classifiers for Computer-Aided Detection in medical images includes providing an image feature training set {(xi, yi)}i=1l, wherein xi?Rd are input feature variables and yi?{?1,1} are class labels, and a cascade of K classifiers to be trained, minimizing, for each classifier k, a first cost function to initialize an ?k0 associated with each classifier k, fixing all classifiers except classifier k and minimizing a second cost function to solve for ?kc for a counter value c using the training dataset {(xik, yi)}i=1l, calculating a third cost function Jc(?lc, . . . , ?Kc) for each classifier k, and comparing Jc with a previous iteration Jc?1, wherein if Jc?Jc?1 is less than a predetermined tolerance, said classifier training is completed.

Abstract: CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability P = ? i = 1 N ? p i y i ? ( 1 - p i ) 1 - y i , wherein N is a number of bags, pi is a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yi is a label where a value of 1 indicates malignancy and 0 otherwise.

Abstract: A method for training classifiers for Computer-Aided Detection in medical images includes providing an image feature training set {(xi, yi)}i=1l, wherein xi?Rd are input feature variables and yi?{?1,1} are class labels, and a cascade of K classifiers to be trained, minimizing, for each classifier k, a first cost function to initialize an ?k0 associated with each classifier k, fixing all classifiers except classifier k and minimizing a second cost function to solve for ?kc for a counter value c using the training dataset {(xik, yi)}i=1l, calculating a third cost function Jc(?lc, . . . , ?Kc) for each classifier k, and comparing Jc with a previous iteration Jc?1, wherein if Jc?Jc?1 is less than a predetermined tolerance, said classifier training is completed.

Abstract: A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.

Abstract: A method of training a classifier for computer aided detection of digitized medical images, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, wherein said features include texture, shape, intensity, and contrast of said region-of-interest, wherein each region-of-interest has been labeled as either malignant or healthy, and training a classifier on said plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the labeled of the associated region-of-interest.

Abstract: Cardiac motion is automatically characterized based on spatial relationship to health. A classifier is trained for the characterization of cardiac motion. Regional wall motion abnormality assessment may be improved by combining information from neighboring segments. The structure or relationship between different segments and associated probabilities of different spatial locations being abnormal given another segment being abnormal are used for classification.

Abstract: A user adjusts the performance for assisted diagnosis. The sensitivity, specificity or both are adjusted as desired by the physician or end-user. By adjusting the trade-offs, a decision support system may be optimized by each user or for each case, possibly avoiding a one-fits-all approach or requiring purchase of different CAD products.

Abstract: A computer-implemented method for processing an image includes identifying a plurality of candidates for an object of interest in the image, extracting a feature set for each candidate, determining a reduced feature set by removing a least one redundant feature from the feature set to maximize a Rayleigh quotient, determining at least one candidate of the plurality of candidates as a positive candidate based on the reduced feature set, and displaying the positive candidate for analysis of the object.

Abstract: A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.

Abstract: A method and device with instructions for analyzing an image data-space includes creating a library of one or more kernels, wherein each kernel from the library of the kernels maps the image data-space to a first data-space using at least one mapping function; and learning a linear combination of kernels in an automatic manner to generate at least one of a classifier and a regressor which is applied to the first data-space. The linear combination of kernels is used to generate a classified image-data space to detect at least one of the candidates in the classified image-data space.

Abstract: CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: Present invention concerns a method for making fine-grained, formable aluminium alloy strips containing (by weight) essentially between 0.5-6.5% Mg, 0-0.50% Si, 0-0.60% Fe, 0-1.2% Mn, 0-0.50% Cr, by twin-roll casting to a thickness ranging between 4 and 6.5 mm and cold rolling the strips to an intermediate gauge and reroll annealing the intermediate gauge material. The reroll-annealed material is then cold rolled to a final sheet gauge followed by a final recrystallizing or back annealing. The combination of controlled casting parameters, controlled amounts of Fe, Si, Mn, Cr and Mg and reroll and final annealing temperatures results in an improved sheet product in terms of finer grain size, higher elongation and formability, age softening and better corrosion resistance. Homogenization may be performed at 420° C. to 550° C. for a period of 4 to 15 hours and recrystallization is performed at 280° C. to 375° C. for a period of not less than 4 hours and not more than 8 hours.