公开(公告)号：EP3991102A1

公开(公告)日：2022-05-04

申请号：EP20734085.2

申请日：2020-06-26

申请人： Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.

发明人： SAMEK, Wojciech ,  LAPUSCHKIN, Sebastian ,  WIEDEMANN, Simon ,  SEEGERER, Philipp ,  YEOM, Seul-Ki ,  MÜLLER, Klaus-Robert ,  WIEGAND, Thomas

IPC分类号： G06N3/08 ,  G06N3/04

公开(公告)号：EP3794515A1

公开(公告)日：2021-03-24

申请号：EP19723445.3

申请日：2019-05-16

申请人： FRAUNHOFER-GESELLSCHAFT zur Förderung der angewandten Forschung e.V.

发明人： SAMEK, Wojciech ,  WIEDEMANN, Simon ,  SATTLER, Felix ,  MÜLLER, Klaus-Robert ,  WIEGAND, Thomas

IPC分类号： G06N3/063 ,  G06N3/08 ,  G06N3/04

公开(公告)号：EP3271863A1

公开(公告)日：2018-01-24

申请号：EP15711738.3

申请日：2015-03-20

申请人： Fraunhofer Gesellschaft zur Förderung der angewandten Forschung E.V. ,  Technische Universität Berlin

发明人： LAPUSCHKIN, Sebastian ,  SAMEK, Wojciech ,  MÜLLER, Klaus-Robert ,  BINDER, Alexander ,  MONTAVON, Grégoire

IPC分类号： G06K9/00 ,  G06T1/20 ,  G06N3/04 ,  G06N3/08

CPC分类号： G06N3/02 ,  G06F17/2765 ,  G06K9/4628 ,  G06K9/6247 ,  G06N3/04 ,  G06N3/084 ,  G10L25/30

摘要： The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

公开(公告)号：EP2756456A1

公开(公告)日：2014-07-23

申请号：EP12770436.9

申请日：2012-09-14

申请人： Technische Universität Berlin ,  Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V. ,  Charite - Universitätsmedizin Berlin

发明人： KLAUSCHEN, Frederick ,  KAWANABE, Motoaki ,  MÜLLER, Klaus-Robert ,  BINDER, Alexander

IPC分类号： G06K9/46 ,  G06K9/62

摘要： Method for the automatic analysis of an image (1, 11, 12, 13) of a biological sample with respect to a pathological relevance, wherein f) local features of the image (1,11,12,13) are aggregated to a global feature of the image (1,11,12,13) using a bag of visual word approach, g) step a) is repeated at least two times using different methods resulting in at least two bag of word feature datasets, , h) computation of at least two similarity measures using the bag of word features obtained from a training image dataset and bag of word features from the image (1, 11, 12, 13) i) the image training dataset comprising a set of visual words, classifier parameters, including kernel weights and bag of word features from the training images, j) the computation of the at least two similarity measures is subject to an adaptive computation of kernel normalization parameters and / or kernel width parameters, f) for each image (1,11,12,13) one score is computed depending on the classifier parameters and kernel weights and the at least two similarity measures, the at least one score being a measure of the certainty of one pathological category compared to the image training dataset, g) for each pixel of the image (1,11,12,13) a pixel-wise score is computed using the classifier parameters, the kernel weights, the at least two similarity measures, the bag of word features of the image (1, 11, 12, 13), all the local features used in the computation of the bag of word features of the image (1, 11, 12, 13) and the pixels used in the computations of the local features, h) the pixel-wise score is stored as a heatmap dataset linking the pixels of the image (1, 11, 12, 13) to the pixel-wise scores.

公开(公告)号：EP1963959A2

公开(公告)日：2008-09-03

申请号：EP06851227.6

申请日：2006-12-08

申请人： Fraunhofer-Gesellschaft zur Förderung der Angewandten Forschung e.V.

发明人： RIECK, Konrad ,  LASKOV, Pavel ,  MÜLLER, Klaus-Robert ,  DÜSSEL, Patrick

IPC分类号： G06F7/02

CPC分类号： G06F7/02 ,  G06F19/22 ,  G06F19/24 ,  G06K9/6201 ,  H04L9/3231 ,  H04L9/3236 ,  H04L63/1416 ,  H04L63/1441

摘要： The invention is concerned with a method and an apparatus for automatic comparison of at least two data sequences characterized in: an evaluation of a local relationship between any pair of subsequences in two or more sequences; an evaluation of a global relationship by means of aggregation of the evaluations of said local relationships.

公开(公告)号：EP1665126A2

公开(公告)日：2006-06-07

申请号：EP04786213.1

申请日：2004-08-17

申请人： Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.

发明人： MÜLLER, Klaus-Robert ,  LASKOV, Pavel ,  TAX, David ,  SCHÄFER, Christin

IPC分类号： G06K9/00

CPC分类号： G06K9/6284

摘要： The invention is concerned with a method for automatic online detection and classification of anomalous objects in a data stream, especially comprising datasets and / or signals, characterized in that a) the detection of at least one incoming data stream (1000) containing normal and anomalous objects, b) automatic construction (2100) of a geometric representation of normality (2200) the incoming objects of the data stream (1000) at a time t1 subject to at least one predefined optimality condition, especially the construction of a hypersurface enclosing a finite number of normal objects, c) online adaptation of the geometric representation ofnormality (2200) in respect to received at least one received object at a time t2 >= t1 , the adaptation being subject to at least one predefined optimality condition, d) online determination of a normality classification (2300) for received objects at t2 in respect to the geometric representation of normality (2200), e) automatic classification of normal objects and anomalous objects based on the generated normality classification (2300) and generating a data set describing the anomalous data for further processing, especially a visual representation.