Autonomous diagnosis of a disorder in a patient from image analysis

A non-transitory computer-readable medium comprising memory with instructions encoded thereon for diagnosing a disease condition in a patient, the instructions, when executed, causing one or more processors to perform operations, the instructions comprising instructions to:

• receive an input image of a portion of a patient's body;
• apply the input image to a feature extraction model, the input image comprising a plurality of locations, the feature extraction model comprising a first trained machine learning model that is configured to generate an output that comprises, for each respective location of each of the plurality of locations in the input image, an indication that the input image contains an object of interest at the respective location, where the object of interest is indicative of a disease;
• apply the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a second trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on taking as input the indications for each of the plurality of locations in the input image as output by the feature extraction model, where the second trained machine learning model is trained by:
• applying ground truth training data that comprises labels about whether a corresponding image depicts a disease and, at each location of a plurality of locations of the corresponding image, indicia that the corresponding image contains an object of interest that is indicative of a disease, to the second machine learning model, and
• adjusting the second machine learning model to fit the ground truth training data, thereby causing the second machine learning model to become trained;
• and
• output the determined diagnosis of the disease condition in the patient obtained from the diagnostic model.

A system for diagnosing a disease condition in a patient, the system comprising:

• memory with instructions encoded thereon;
• and
• one or more processors that, when executing the instructions, are caused to perform operations comprising:
• receiving an input image of a portion of a patient's body;
• applying the input image to a feature extraction model, the input image comprising a plurality of locations, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of each of the plurality of locations in the input image, an indication that the input image contains an object of interest at the respective location, where the object of interest is indicative of a disease;
• applying the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a second trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on taking as input the indications for each of the plurality of locations in the input image as output by the feature extraction model, where the second trained machine learning model is trained by:
• applying ground truth training data that comprises labels about whether a corresponding image depicts a disease and, at each location of a plurality of locations of the corresponding image, indicia that the corresponding image contains an object of interest that is indicative of a disease, to the second machine learning model, and
• adjusting the second machine learning model to fit the ground truth training data, thereby causing the second machine learning model to become trained;
• and
• outputting the determined diagnosis of the disease condition in the patient obtained from the diagnostic model.