Patent application number | Description | Published |
20120243788 | DYNAMIC RADIAL CONTOUR EXTRACTION BY SPLITTING HOMOGENEOUS AREAS - Systems and methods for extracting a radial contour around a given point in an image includes providing an image including a point about which a radial contour is to be extracted around. A plurality of directions around the point and a plurality of radius lengths for each direction are provided. Local costs are determined for all radius lengths for each direction by comparing texture variances at each radius length with the texture variance at a further radius length. A radius length is determined, using a processor, for each direction based on the accumulated value of the local costs to provide a radial contour. | 09-27-2012 |
20130034279 | CLOUD-BASED DIGITAL PATHOLOGY - A method and systems for cloud-based digital pathology include scanning received slides that include a pathology sample to produce a sample image in a shared memory, analyzing the sample image using one or more execution nodes, each including one or more processors, according to one or more analysis types to produce intermediate results, transmitting some or all of the sample image to a client device, further analyzing the sample image responsive to a request from the client device to produce a final analysis based on the intermediate results, and transmitting the final analysis to the client device. | 02-07-2013 |
20130034301 | INTERACTIVE ANALYTICS OF DIGITAL HISTOLOGY SLIDES - Methods and systems for interactive image analysis include receiving a selection of a region of an image and a request for analysis of the selection at an interface layer, transferring the selection and the request to an interpretation layer for analysis, dividing the selected region of the image into a plurality of sub-sections optimized for parallel computation to provide an analysis result that minimizes perceptible delay between receiving the request and receipt of results, analyzing the sub-sections using one or more execution nodes using a copy of the image stored in a shared memory, and providing combined analysis results to the interface layer for display. | 02-07-2013 |
20130034304 | DIGITAL PATHOLOGY SYSTEM WITH LOW-LATENCY ANALYTICS - Methods and systems for digital pathology with low-latency analytics include determining potential regions of interest within an image in accordance with one or more high-priority analyses, dividing the potential regions of interest into a plurality of sub-sections optimized for parallel computation, analyzing the sub-sections using one or more execution nodes, each including one or more processors, using a copy of the image stored in a shared memory according to the one or more high-priority analyses, and storing an intermediate analysis result based on analysis results from the one or more execution nodes in a shared memory. | 02-07-2013 |
20130315465 | Whole Tissue Classifier for Histology Biopsy Slides - Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue. | 11-28-2013 |
20140122388 | QUERY GENERATION AND TIME DIFFERENCE FEATURES FOR SUPERVISED SEMANTIC INDEXING - Semantic indexing methods and systems are disclosed. One such method is directed to training a semantic indexing model by employing an expanded query. The query can be expanded by merging the query with documents that are relevant to the query for purposes of compensating for a lack of training data. In accordance with another exemplary aspect, time difference features can be incorporated into a semantic indexing model to account for changes in query distributions over time. | 05-01-2014 |
20140180977 | Computationally Efficient Whole Tissue Classifier for Histology Slides - Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state. | 06-26-2014 |
20140236577 | Semantic Representations of Rare Words in a Neural Probabilistic Language Model - Systems and methods are disclosed for representing a word by extracting n-dimensions for the word from an original language model; if the word has been previously processed, use values previously chosen to define an (n+m) dimensional vector and otherwise randomly selecting m values to define the (n+m) dimensional vector; and applying the (n+m) dimensional vector to represent words that are not well-represented in the language model. | 08-21-2014 |
20140236578 | Question-Answering by Recursive Parse Tree Descent - Systems and methods are disclosed to answer free form questions using recursive neural network (RNN) by defining feature representations at every node of a parse trees of questions and supporting sentences, when applied recursively, starting with token vectors from a neural probabilistic language model; and extracting answers to arbitrary natural language questions from supporting sentences. | 08-21-2014 |