A computer-implemented method for classifying a lesion, comprising - receiving (REC-IMG.1) a first medical image (IMG.1) of an examination volume, wherein the first medical image (IMG.1) corresponds to a first examination time (t.1), - receiving (REC-IMG.2) a second medical image (IMG.2) of the examination volume, wherein the second medical image (IMG.2) corresponds to a second examination time (t.2) being different from the first examination time (t.2), - determining (DET-LA.1) a first lesion area (LA.1) corresponding to the lesion within the first medical image (IMG.1), - determining (DET-RF) a registration function (RF(t.2, t.1)) based on a comparison of the first medical image (IMG.1) and the second medical image (IMG.2), - determining (DET-LA.2) a second lesion area (LA.2) within the second medical image (IMG.2) based on the registration function (RF(t.2, t.1)) and the first lesion area (LA.1), - applying (APL-TCF) a trained classifying function (TCF) to first input data (ID.1) and second input data (ID.2), thereby generating output data (OD.1, OD.2), wherein the first input data (ID.1) is based on the first lesion area (LA.1), wherein the second input data (ID.2) is based on the second lesion area (LA.2), wherein the trained classifying function (TCF) is a recurrent neural network, wherein the first input data (ID.1) and the second input data (ID.2) are independently used as input data for the recurrent neural network, wherein information from applying the recurrent neural network to the second input data (ID.2) is stored within a hidden state, and is used as additional information when applying the recurrent network to the first input data, and wherein the output data (OD.1, OD.2) of the recurrent neural network depends on the hidden state, - determining (DET-LC) a lesion classification based on the output data (OD.1, OD.2). - receiving (REC-IMG.1) a first medical image (IMG.1) of an examination volume, wherein the first medical image (IMG.1) corresponds to a first examination time (t.1), - receiving (REC-IMG.2) a second medical image (IMG.2) of the examination volume, wherein the second medical image (IMG.2) corresponds to a second examination time (t.2) being different from the first examination time (t.2), - determining (DET-LA.1) a first lesion area (LA.1) corresponding to the lesion within the first medical image (IMG.1), - determining (DET-RF) a registration function (RF(t.2, t.1)) based on a comparison of the first medical image (IMG.1) and the second medical image (IMG.2), - determining (DET-LA.2) a second lesion area (LA.2) within the second medical image (IMG.2) based on the registration function (RF(t.2, t.1)) and the first lesion area (LA.1), - applying (APL-TCF) a trained classifying function (TCF) to first input data (ID.1) and second input data (ID.2), thereby generating output data (OD.1, OD.2), wherein the first input data (ID.1) is based on the first lesion area (LA.1), wherein the second input data (ID.2) is based on the second lesion area (LA.2), wherein the trained classifying function (TCF) is a recurrent neural network, wherein the first input data (ID.1) and the second input data (ID.2) are independently used as input data for the recurrent neural network, wherein information from applying the recurrent neural network to the second input data (ID.2) is stored within a hidden state, and is used as additional information when applying the recurrent network to the first input data, and wherein the output data (OD.1, OD.2) of the recurrent neural network depends on the hidden state, - determining (DET-LC) a lesion classification based on the output data (OD.1, OD.2). <EOS>