A method for medical diagnostics, comprising: receiving (1010, 1210) three-dimensional, 3D, volume data of a part of a patient's body; generating (1020, 1220) two-dimensional, 2D, slices including cross-sections of the 3D volume data cut along at least one cross-section cutting direction; determining whether a lesion in each of the 2D slices is benign or malignant and outputting results indicative thereof; selecting a number of the 2D slices based on the results; and making a final determination whether the lesion is benign or malignant based on the selected 2D slices; characterized in that the determining comprises applying each of the 2D slices to a diagnostic model to determine whether the lesion in each of the 2D slices is benign or malignant, involves applying each of the 2D slices to a single diagnostic model that is generated based on a cross-section cutting direction or to a generated integrated diagnostic model comprising a combination of diagnostic models corresponding to different cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant; or applying the 2D slices to respective diagnostic models that are generated based on cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant. receiving (1010, 1210) three-dimensional, 3D, volume data of a part of a patient's body; generating (1020, 1220) two-dimensional, 2D, slices including cross-sections of the 3D volume data cut along at least one cross-section cutting direction; determining whether a lesion in each of the 2D slices is benign or malignant and outputting results indicative thereof; selecting a number of the 2D slices based on the results; and making a final determination whether the lesion is benign or malignant based on the selected 2D slices; characterized in that the determining comprises applying each of the 2D slices to a diagnostic model to determine whether the lesion in each of the 2D slices is benign or malignant, involves applying each of the 2D slices to a single diagnostic model that is generated based on a cross-section cutting direction or to a generated integrated diagnostic model comprising a combination of diagnostic models corresponding to different cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant; or applying the 2D slices to respective diagnostic models that are generated based on cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant. applying each of the 2D slices to a diagnostic model to determine whether the lesion in each of the 2D slices is benign or malignant, involves applying each of the 2D slices to a single diagnostic model that is generated based on a cross-section cutting direction or to a generated integrated diagnostic model comprising a combination of diagnostic models corresponding to different cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant; or applying the 2D slices to respective diagnostic models that are generated based on cross-section cutting directions of the 2D slices to determine whether the lesion in each of the 2D slices is benign or malignant.