| CPC G06T 7/0012 (2013.01) [G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06V 10/462 (2022.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30068 (2013.01); G06V 2201/03 (2022.01)] | 14 Claims |

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1. A computer-implemented method of determining a continuous value prediction for one or more digital medical images, the method comprising:
receiving one or more digital medical images of a sample associated with a patient, the one or more digital medical images comprising pixels and/or voxels;
appending spatial information to the pixels and/or voxels of the one or more digital medical images, wherein the spatial information is appended by concatenating coordinates of each pixel and/or voxel to each pixel and/or voxel;
determining, by providing the appended digital medical images to a trained machine learning system, a continuous value prediction of a level of one or more biomarkers, the trained machine learning system having been trained directly using a plurality of medical images and a continuous score prediction loss function; and
providing the continuous value prediction for output to a display and/or storage,
wherein determining a continuous value prediction based on analyzing the pixels and/or voxels with appended spatial information comprises:
incorporating a predicted genomic expression, a predicted number of cells having a particular cell type, a predicted number of cells having a particular cell sub-type, a predicted protein expression, a predicted measurement of physical size, or a combination thereof; and
incorporating a location of one or more cells, a spatial distribution of predicted genomic expression, a spatial distribution of predicted protein expression, or a combination thereof.
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