Method for classifying tissue samples, with which - annotated training data is generated from at least one known positive tissue sample that has a specified property and at least one known negative tissue sample that does not have the specified property, wherein - the annotated training data is provided to an automatic classifier, - the classifier is trained using the annotated training data to identify whether a tissue sample from this patient has the specified property, - to delimit a tissue region affected by the specified property from a plurality of unknown tissue samples for which it is not known whether they have the specified property, input data is generated for the classifier, - the unknown tissue samples are automatically classified by means of the classifier with respect to the specified property using the input data; - an image of the affected tissue region and the environment thereof (3, 4, 18) with respective image coordinates of sampling locations of the classified tissue samples are provided to an analysis device by electronic means, and - on the basis thereof, the analysis device automatically ascertains a suggestion for a sampling location (17, 20) for a further unknown tissue sample for delimiting the affected tissue region and outputs this to a user, characterised in that the positive and negative tissue samples (12, 13), from which the annotated training data is generated, originate from the same patient, the classifier is trained patient-specifically for the same patient from whom the known positive and negative tissue samples (12, 13) originate and the unknown tissue samples likewise originate from the same patient, from which the known tissue samples (12, 13) originate. - annotated training data is generated from at least one known positive tissue sample that has a specified property and at least one known negative tissue sample that does not have the specified property, wherein - the annotated training data is provided to an automatic classifier, - the classifier is trained using the annotated training data to identify whether a tissue sample from this patient has the specified property, - to delimit a tissue region affected by the specified property from a plurality of unknown tissue samples for which it is not known whether they have the specified property, input data is generated for the classifier, - the unknown tissue samples are automatically classified by means of the classifier with respect to the specified property using the input data; - an image of the affected tissue region and the environment thereof (3, 4, 18) with respective image coordinates of sampling locations of the classified tissue samples are provided to an analysis device by electronic means, and - on the basis thereof, the analysis device automatically ascertains a suggestion for a sampling location (17, 20) for a further unknown tissue sample for delimiting the affected tissue region and outputs this to a user, characterised in that the positive and negative tissue samples (12, 13), from which the annotated training data is generated, originate from the same patient, the classifier is trained patient-specifically for the same patient from whom the known positive and negative tissue samples (12, 13) originate and the unknown tissue samples likewise originate from the same patient, from which the known tissue samples (12, 13) originate. the positive and negative tissue samples (12, 13), from which the annotated training data is generated, originate from the same patient, the classifier is trained patient-specifically for the same patient from whom the known positive and negative tissue samples (12, 13) originate and the unknown tissue samples likewise originate from the same patient, from which the known tissue samples (12, 13) originate.