A system comprising a processing apparatus configured to perform a training method to train a model to generate from input data at least one predicted output indicating the presence or absence of a pathology of interest, the training method comprising: receiving a plurality of training data sets; receiving a selection of a first characteristic from a set of characteristics of the training data sets, wherein the first characteristic is considered to be relevant to prediction of the at least one predicted output; receiving a selection of a second characteristic from the set of characteristics, wherein the second characteristic is considered to be less relevant or irrelevant to prediction of the at least one predicted output and comprises a characteristic that is a spurious consequence of data acquisition, and so is unrelated to the pathology of interest; and training the model, the training of the model comprising: performing positive supervision of the model using the first characteristic such that the training of the model to predict the at least one predicted output is sensitive to values for the first characteristic; and performing negative supervision of the model using the second characteristic such that the training of the model to predict the at least one predicted output is insensitive to values for the second characteristic, wherein: each of the plurality of training data sets comprises respective medical image data comprising a set of pixel or voxel intensities for an array of pixel or voxel positions; the training of the model further comprises performing supervision of the model using the medical image data such that the model is trained to use the medical image data in prediction; and the predicted output comprises a segmentation comprising classification of the pixels or voxels, or at least one label. receiving a plurality of training data sets; receiving a selection of a first characteristic from a set of characteristics of the training data sets, wherein the first characteristic is considered to be relevant to prediction of the at least one predicted output; receiving a selection of a second characteristic from the set of characteristics, wherein the second characteristic is considered to be less relevant or irrelevant to prediction of the at least one predicted output and comprises a characteristic that is a spurious consequence of data acquisition, and so is unrelated to the pathology of interest; and training the model, the training of the model comprising: performing positive supervision of the model using the first characteristic such that the training of the model to predict the at least one predicted output is sensitive to values for the first characteristic; and performing negative supervision of the model using the second characteristic such that the training of the model to predict the at least one predicted output is insensitive to values for the second characteristic, wherein: performing positive supervision of the model using the first characteristic such that the training of the model to predict the at least one predicted output is sensitive to values for the first characteristic; and performing negative supervision of the model using the second characteristic such that the training of the model to predict the at least one predicted output is insensitive to values for the second characteristic, wherein: each of the plurality of training data sets comprises respective medical image data comprising a set of pixel or voxel intensities for an array of pixel or voxel positions; the training of the model further comprises performing supervision of the model using the medical image data such that the model is trained to use the medical image data in prediction; and the predicted output comprises a segmentation comprising classification of the pixels or voxels, or at least one label.