S.d. Sight Diagnostics Ltd.

International Business Machines Corporation

S.d. Diagnostics Ltd.

Sight Diagnostics Ltd.

Yonatan Bilu

Joseph Joel Pollak

Noam Yorav-Raphael

Yuval Greenfield

Noam Slonim

Daniel Levner

Ehud Aharoni

Ido Bachelet

Dan Gutfreund

Ran Levy

Ruty Rinott

Daniel Hershcovich

Arnon Houri Yafin

Tamar Lavee

Liat Ein Dor

Noam Yorav Raphael

Indrajit Bhattacharya

Vikas Raykar

Arnon Yafin

Dalia Krieger

Godbole Shantanu

Noam Yorav-raphael

Apparatus and methods are described for use with a cell sample that includes a plurality of cells. A series of images associated with a series of depth levels of the cell sample are acquired, by performing a depth scan of cell sample with a microscope. One of the depth levels is identified as being an optimum focal plane for imaging one or more entities within the cell sample using the microscope, under a first illumination condition. The cell sample is imaged under a second illumination condition that is different from the first illumination condition, using the microscope, by focusing the microscope at an investigative depth level that is based on the identified depth level. Other applications are also described.

Method and system for imaging a cell sample

Apparatus and methods for use with a blood sample are described. The blood sample is stained with a first dye that predominantly stains DNA, and a second dye that stains at least one other cellular component being different from DNA. A plurality of images of the blood sample are acquired. A candidate object is identified as being a candidate of a given entity. A first stained area, which is stained by the first dye and which is disposed within the candidate object, is identified. A second stained area, which is stained by the second dye and which is disposed within the candidate object, is identified. The entity is detected by determining that features of the stained areas satisfy predetermined criteria associated with the entity. Other applications are also described.

Methods and systems for detecting entities in a biological sample

Apparatus and methods are described including a microscope system configured to acquire first and second images of a blood sample at respective times. A computer processor determines whether, between acquisitions of the first and second images, there was motion of a given entity within the sample, by comparing the first and second images to one another. At least partially in response thereto, the computer processor distinguishes between the given entity being a non-parasitic entity and the given entity being a parasite, and generates an output on the output device, at least partially in response thereto. Other applications are also described.

Distinguishing between entities in a blood sample

Apparatus and methods are described including successively acquiring a plurality of microscopic images of a portion of a blood sample, and tracking motion of pixels within the successively acquired microscopic images. Trypomastigote parasite candidates within the blood sample are identified, by identifying pixel motion that is typical of trypomastigote parasites. It is determined that the blood sample is infected with trypomastigote parasites, at least partially in response thereto. An output is generated indicating that that the blood sample is infected with trypomastigote parasites. Other applications are also described.

Apparatus and method for analyzing a bodily sample

Apparatus and methods are described for use with a cell sample that includes a plurality of cells disposed in a monolayer. A series of images associated with a series of depth levels of the cell sample are acquired, by performing a depth scan of cell sample with a microscope. One of the depth levels is identified as being an optimum focal plane for imaging one or more entities within the cell sample using the microscope, by identifying that a drop in image contrast occurs at the identified depth level relative to the image contrast of images acquired at depth levels within a given range of distances from the identified depth level. The cell sample is imaged using the microscope, by focusing the microscope at an investigative depth level that is based on the identified depth level. Other applications are also described.

Apparatus and methods for use with a blood sample are described. The blood sample is stained with Hoechst stain and Acridine Orange stain. A plurality of images of the blood sample are acquired. An object is identified as being a white blood cell candidate. A first stained area, which is stained by the Hoechst stain and which is disposed within the white blood cell candidate, is identified. A second stained area, which is stained by the Acridine Orange stain and which is disposed within the white blood cell candidate, is identified. A white blood cell is detected by determining that structural features of the stained areas satisfy predetermined criteria associated with a white blood cell. Other applications are also described.

Embodiments may provide techniques for clustering using predefined anchors that take into account the knowledge about the anchors. For example, a method of clustering processing may be implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: receiving a plurality of points of data to be clustered and a plurality of predefined anchor data points and clustering the plurality of points of data and at least a subset of the predefined anchor data points. Further, the method may include creating a similarity function where anchor points pull stronger than regular points, such that they function as attractors.

Clustering items around predefined anchors

Apparatus and methods are described for use with a sample that includes a plurality of red blood cells. A series of images associated with a series of depth levels of the sample are acquired, by performing a depth scan of the sample with a microscope. One of the depth levels is identified as being an optimum focal plane for imaging one or more entities within the sample using the microscope, by identifying that a drop in image contrast occurs at the identified depth level. The sample is imaged using the microscope, by focusing the microscope at an investigative depth level that is based on the identified depth level. Other applications are also described.

A system for identifying in a corpus of documents at least one argument relevant to an identified topic, comprising at least one hardware processor adapted to: producing a plurality of topic-related sentences relevant to the identified topic, each extracted from a document of the corpus of documents; producing a plurality of synthetic documents, each created by appending to a sentence of the plurality of topic-related sentences an identified amount of other sentences extracted from the respective document the topic-related sentence was extracted therefrom; identifying at least one argument relevant to the identified topic by inputting each of the plurality of synthetic documents to at least one machine learning model trained to identify an argument in response to a document; and outputting the at least one argument.

System and method for argument retrieval

A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and content relevant to the TUC; detecting one or more claims relevant to the TUC in the content, based on detection of boundaries of the claims in the content; and outputting a list of said detected one or more claims.

Automatic detection of claims with respect to a topic

Apparatus and methods are described for use with a cell sample. For each of one or more imaging fields of the cell sample, a depth scan of the cell sample is performed using a microscope. One of the depth levels is identified as being an optimum focal plane for imaging one or more entities within the sample using the microscope, at least partially in response to detecting that the depth level corresponds to a drop in image contrast relative to image contrast at other depth levels. Based upon the depth level identified as being the optimum focal plane for each of the one or more imaging fields of the cell sample, a scanning depth interval over which to perform a depth scan of the cell sample at a further imaging field is defined. Other applications are also described.

Apparatus and methods are described for use with a digital camera that is configured to acquire images of a bodily sample. Two or more stains are configured to stain the bodily sample. A computer processor drives the digital camera to acquire, for each of a plurality of imaging fields of the bodily sample, two or more digital images, at least one of the images being acquired under brightfield lighting conditions, and at least one of the images being acquired under fluorescent lighting conditions. The computer processor performs image processing on the digital images, by extracting visual classification features from the digital images and analyzing the extracted visual classification features. The computer processor outputs a result of the image processing that includes an indication of one or more entities that are contained within the sample. Other applications are also described.

Methods, systems and computer program products relating to digital microscopy are disclosed. A digital microscopy method may comprise capturing a plurality of overlapping images of a sample, wherein the capturing of at least one of the plurality of images is performed while the sample and a focal plane are in relative motion along an optical axis at a speed greater than zero; and processing the plurality of images using a reference criterion to determine a reference relative arrangement of the focal plane and the sample along the optical axis.

Digital microscopy systems, methods and computer program products

A computer-implemented method, computerized apparatus and computer program product for claim generation, the method comprising: selecting at least one subject according to a given topic; selecting at least one verb from a first data source; selecting at least one object from a second data source; generating one or more candidate claim sentences, each of which composed of a subject selected from the at least one subject, a verb selected from the at least one verb and an object selected from the at least on object; and determining validity of the candidate claim sentences using a machine learning process.

Claim generation

Disclosed herein, inter alia, is method, kit and systems for detecting a pathogen infection in a bodily sample, the method comprising (i) staining said bodily sample with two or more dyes, comprising at least one dye predominantly staining DNA to thereby provide differential staining between DNA and at least one other cellular component being different from DNA; (ii) identifying at least a first stained area comprising the DNA, if exists in the sample, and at least one other stained area comprising the other cellular component; (iii) extracting structural features for the first stained area and the at least one other stained area, said structural features comprise at least one of (i) size of at least one of the first stained area and one other stained area and (ii) location of said first stained area and said at least one other stained area one with respect to the other; and (iv) determining the presence of a suspected pathogen in the bodily sample if a first stained area was identified and said structural features conform to structural features predetermined as characterizing the suspected pathogen.

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Jerusalem, Israel

Yonatan Bilu