Abstract: Systems, methods, and apparatuses for analyzing histopathology images to determine the presence of certain predetermined abnormalities. The system processes histopathology images to identify/highlight regions of interest (e.g., a region that may comprise parts of a tumor, cancerous cells, or other predetermined abnormality) for subsequent review by a pathologist or other trained professional. For example, the system may process histopathology images of H&E-stained lymph node tissue to identify potentially cancerous cells within the histopathology images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: Methods disclosed herein include: (a) determining positions of a plurality of cells based on one or more images of the cells; (b) for at least some of the plurality of cells, generating a matrix that includes two-dimensional information about positions of neighboring cells, and determining one or more numerical features based on the information in the matrix; and (c) classifying the at least some of the plurality of cells as belonging to at least one of multiple classes based on the numerical features.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: Methods disclosed herein include: (a) determining positions of a plurality of cells based on one or more images of the cells; (b) for at least some of the plurality of cells, generating a matrix that includes two-dimensional information about positions of neighboring cells, and determining one or more numerical features based on the information in the matrix; and (c) classifying the at least some of the plurality of cells as belonging to at least one of multiple classes based on the numerical features.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.