An image processing method comprising: receiving a first image (100, 300) of an object; extracting, with a trained machine learning system, a feature of the first image or of a physiological structure shown in the first image, the first image being an input to the trained machine learning system; classifying, with a first trained classifier, the first image based on the extracted feature, the extracted feature being an input to the first trained classifier; and determining a label and/or score of the first image based on the classification; receiving a second image (100, 300) of the object; extracting, with the trained machine learning system, a feature of the second image or of a physiological structure shown in the second image, the second image being an input to the trained machine learning system; classifying, with a second trained classifier, the second image based on the extracted feature of the second image, the extracted feature of the second image being an input to the second trained classifier; and determining a label and/or score of the second image based on the classification, wherein the first image and the second image are patches, and wherein training and mapping of patches to classifiers is based on a type, location, size and/or resolution of the patches, wherein each classifier is trained with images from one or more distinct spatial locations, types, sizes and/or resolutions, each corresponding to a different patch, wherein each of the plurality of patches are from a distinct spatial region of a common cross-sectional image of the object, and the first trained classifier and the second trained classifier are component classifiers of an ensemble classifier, the first trained classifier being trained with training image patches from the spatial region corresponding to the first image, and the second trained classifier being trained with training image patches from the spatial regions corresponding to the second image. receiving a first image (100, 300) of an object; extracting, with a trained machine learning system, a feature of the first image or of a physiological structure shown in the first image, the first image being an input to the trained machine learning system; classifying, with a first trained classifier, the first image based on the extracted feature, the extracted feature being an input to the first trained classifier; and determining a label and/or score of the first image based on the classification; receiving a second image (100, 300) of the object; extracting, with the trained machine learning system, a feature of the second image or of a physiological structure shown in the second image, the second image being an input to the trained machine learning system; classifying, with a second trained classifier, the second image based on the extracted feature of the second image, the extracted feature of the second image being an input to the second trained classifier; and determining a label and/or score of the second image based on the classification, wherein the first image and the second image are patches, and wherein training and mapping of patches to classifiers is based on a type, location, size and/or resolution of the patches, wherein each classifier is trained with images from one or more distinct spatial locations, types, sizes and/or resolutions, each corresponding to a different patch, wherein each of the plurality of patches are from a distinct spatial region of a common cross-sectional image of the object, and the first trained classifier and the second trained classifier are component classifiers of an ensemble classifier, the first trained classifier being trained with training image patches from the spatial region corresponding to the first image, and the second trained classifier being trained with training image patches from the spatial regions corresponding to the second image.