Computer classification of biological tissue

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:

• obtain a set of raw time-series images of an examination area of a biological tissue, wherein the set of raw time-series images corresponds to a discrete time period in which portions of the biological tissue undergo a transient optical response responsive to topical application of a pathology differentiating agent to the examination area of the biological tissue;
• obtain time data associated with the set of raw time-series images;
• process the set of raw time-series images and the time data to create a modified set of time-series images such that they are aligned with respect to each other;
• extract a set of pixel values against time for multiple pixels from the modified set of time-series images and the time data;
• input the set of pixel values against time for multiple pixels into an algorithm that identifies segments of the biological tissue based on analysis indices that are derived from a curve of intensity value against time for each pixel, the identified plurality of segments of the biological tissue being obtained as an output of the algorithm;
• input the set of pixel values against time for multiple pixels and the identified plurality of segments of the biological tissue into a trained neural network that identifies a plurality of segments of the tissue and characterizes a likelihood that the biological tissue corresponds to a disease state, the plurality of segments being identified based on common disease state, the trained neural network being separate from the algorithm that identifies the plurality of segments of the biological tissue;
• obtain one or more classifications of each of the plurality of segments of the biological tissue from the trained neural network;
• and
• output a cancer assessment for at least one of the plurality of segments based on the one or more classifications output by the trained neural network, wherein the cancer assessment indicates one of a precancerous disease state, a cancerous disease state, or a non-cancerous state.

A computing system operative for classification of a tissue, comprising a processor configured to:

• obtain a set of raw time-series images of an examination area of a biological tissue, wherein the set of raw time-series images corresponds to a discrete time period in which portions of the biological tissue undergo a transient optical response responsive to topical application of a pathology differentiating agent to the examination area of the biological tissue;
• obtain time data associated with the set of raw time-series images;
• process the set of raw time-series images and the time data to create a modified set of time-series images such that they are aligned with respect to each other;
• extract a set of pixel values against time for multiple pixels from the modified set of time-series images and the time data;
• input the set of pixel values against time for multiple pixels into an algorithm that identifies segments of the biological tissue based on analysis indices that are derived from a curve of intensity value against time for each pixel, the identified plurality of segments of the biological tissue being obtained as an output of the algorithm;
• input the set of pixel values against time for multiple pixels and the identified plurality of segments of the biological tissue into a trained neural network that identifies a plurality of segments of the tissue and characterizes a likelihood that the biological tissue corresponds to a disease state or lesion, the trained neural network being separate from the algorithm that identifies the plurality of segments of the biological tissue;
• obtain one or more classifications of each of the plurality of segments of the biological tissue from the trained neural network;
• and
• output a cancer assessment for at least one of the plurality of segments based on the one or more classifications output by the trained neural network, wherein the cancer assessment indicates one of a precancerous disease state, a cancerous disease state, or a non-cancerous state.