Abstract: The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.

Abstract: In one example, a method includes receiving, by a computing system, context information associated with a computing device; inferring, by the computing system and based on the context information, an action of a user of the computing device, the action associated with at least one entity; determining, by the computing system and based on stored attribute information associated with the at least one entity, and based on a stored set of rules associated with the inferred action, that the inferred action is not advisable; and responsive to determining that the inferred action is not advisable, outputting, by the computing system and for display on the computing device, notification data indicating that the inferred action is not advisable.

Abstract: In one example, a method includes receiving, by a computing system, context information associated with a computing device; inferring, by the computing system and based on the context information, an action of a user of the computing device, the action associated with at least one entity; determining, by the computing system and based on stored attribute information associated with the at least one entity, and based on a stored set of rules associated with the inferred action, that the inferred action is not advisable; and responsive to determining that the inferred action is not advisable, outputting, by the computing system and for display on the computing device, notification data indicating that the inferred action is not advisable.

Abstract: The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.

Abstract: An example imaging apparatus comprises a light source, an imaging spectrometer, an image sensor, control circuitry, and processing circuitry. The light source generates power for delivery at a molecular sample, which can include at least 100 mW. The imaging spectrometer separates light emitted from the molecular sample into a plurality of different component wavelengths. The control circuitry causes the image sensor to scan one or more regions of the molecular sample while the imaging spectrometer is aligned with the image sensor and collects hyperspectral image data of the molecular sample from the light emitted that corresponds to the plurality of different component wavelengths. The processing circuitry performs an image processing pipeline by transforming the hyperspectral image data into data that is representative of a quantification of emitters, absorbers, and/or scatterers present in the one or more regions of the molecular sample.

Abstract: In one example, a method includes receiving, by a computing system, context information associated with a computing device; inferring, by the computing system and based on the context information, an action of a user of the computing device, the action associated with at least one entity; determining, by the computing system and based on stored attribute information associated with the at least one entity, and based on a stored set of rules associated with the inferred action, that the inferred action is not advisable; and responsive to determining that the inferred action is not advisable, outputting, by the computing system and for display on the computing device, notification data indicating that the inferred action is not advisable.

Abstract: An example imaging apparatus comprises a light source, an imaging spectrometer, an image sensor, control circuitry, and processing circuitry. The light source generates power for delivery at a molecular sample, which can include at least 100 mW. The imaging spectrometer separates light emitted from the molecular sample into a plurality of different component wavelengths. The control circuitry causes the image sensor to scan one or more regions of the molecular sample while the imaging spectrometer is aligned with the image sensor and collects hyperspectral image data of the molecular sample from the light emitted that corresponds to the plurality of different component wavelengths. The processing circuitry performs an image processing pipeline by transforming the hyperspectral image data into data that is representative of a quantification of emitters, absorbers, and/or scatterers present in the one or more regions of the molecular sample.

Abstract: In one example, a method includes receiving, by a computing system, context information associated with a computing device; inferring, by the computing system and based on the context information, an action of a user of the computing device, the action associated with at least one entity; determining, by the computing system and based on stored attribute information associated with the at least one entity, and based on a stored set of rules associated with the inferred action, that the inferred action is not advisable; and responsive to determining that the inferred action is not advisable, outputting, by the computing system and for display on the computing device, notification data indicating that the inferred action is not advisable.