Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.

Abstract: A system associated with quantifying a density level of tumor-infiltrating lymphocytes, based on prediction of reconstructed TIL information associated with tumoral tissue image data during pathology analysis of the tissue image data is disclosed. The system receives digitized diagnostic and stained whole-slide image data related to tissue of a particular type of tumoral data. Defined are regions of interest that represents a portion of, or a full image of the whole-slide image data. The image data is encoded into segmented data portions based on convolutional autoencoding of objects associated with the collection of image data. The density of tumor-infiltrating lymphocytes is determined of bounded segmented data portions for respective classification of the regions of interest. A classification label is assigned to the regions of interest. It is determined whether an assigned classification label is above a pre-determined threshold probability value of lymphocyte infiltrated.

Abstract: A system and method for automatically observing and counting cells without using a stain or a fluorescent material. The system includes an optical microscope having a sensor that provides an electrical signal representative of a field of view. The microscope is motorized so as to allow automatic change of focus. A sample containing cells to be analyzed is provided. No stain or fluorescent substance is used. When the microscope is operated in a deliberately out-of-focus condition, cells appear to have either a bright or a dark spot that can be used to report the number of cells in the sample. The intensity variation detected in images acquired in different focal planes is used to identify cell shapes using image analysis software such as CellProfiler. A result is reported in any convenient format, such as a false color image.

Abstract: A system and method for automatically observing and counting cells without using a stain or a fluorescent material. The system includes an optical microscope having a sensor that provides an electrical signal representative of a field of view. The microscope is motorized so as to allow automatic change of focus. A sample containing cells to be analyzed is provided. No stain or fluorescent substance is used. When the microscope is operated in a deliberately out-of-focus condition, cells appear to have either a bright or a dark spot that can be used to report the number of cells in the sample. The intensity variation detected in images acquired in different focal planes is used to identify cell shapes using image analysis software such as CellProfiler. A result is reported in any convenient format, such as a false color image.

Abstract: Embodiments of the invention encompass methods for modeling of complex systems, which include, but are not limited to gene regulatory networks, biological systems, and the like. Other embodiments of the invention include the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. The embodiments discussed utilize methods that model the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene mutation as well as intervention in order to elicit desired network behavior.

Abstract: Embodiments of the invention encompass methods for modeling of complex systems, which include, but are not limited to gene regulatory networks, biological systems, and the like. Other embodiments of the invention include the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. The embodiments discussed utilize methods that model the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene mutation as well as intervention in order to elicit desired network behavior.