A computer-implemented method for medical image interpretation, the method comprising: providing a medical image (Img_m) to a convolutional neural network model (M1, M2, M3), wherein the convolutional neural network model (M1, M2, M3) comprises a feature extraction part (31, 41), a first classifier, and N second classifiers (33_1 to 33_N), with N being an integer equal to or larger than one; generating N feature maps (Fm_1 to Fm_N) by using the feature extraction part (31, 41) of the convolutional neural network model (M1, M2, M3); obtaining N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8) of N symptoms of a disease based on the N feature maps (Fm_1 to Fm_N) through the N second classifiers (33_1 to 33_N), wherein each one of the N obtained symptom interpretation results indicates the presence or severity of the respective symptom in the medical image; and obtaining a disease interpretation result (36, 46) of the disease based on the N feature maps (Fm_1 to Fm_N) through the first classifier, wherein the disease interpretation result indicates the severity of the disease; wherein the convolutional neural network model (M1, M2, M3) further comprises a symptom feature extraction part (48_1 to 48_8), and wherein the step of obtaining N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8) of the N symptoms of the disease based on the N feature maps (Fm_1 to Fm_N) through the N second classifiers (33_1 to 33_N) comprises: inputting one of the N feature maps (Fm_1 to Fm_N) to the symptom feature extraction part (48_1 to 48_8) for obtaining at least one symptom feature map; and inputting the at least one symptom feature map to one of the N second classifiers (33_1 to 33_N) so that the one of the N second classifiers (33_1 to 33_N) outputs one of the N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8). providing a medical image (Img_m) to a convolutional neural network model (M1, M2, M3), wherein the convolutional neural network model (M1, M2, M3) comprises a feature extraction part (31, 41), a first classifier, and N second classifiers (33_1 to 33_N), with N being an integer equal to or larger than one; generating N feature maps (Fm_1 to Fm_N) by using the feature extraction part (31, 41) of the convolutional neural network model (M1, M2, M3); obtaining N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8) of N symptoms of a disease based on the N feature maps (Fm_1 to Fm_N) through the N second classifiers (33_1 to 33_N), wherein each one of the N obtained symptom interpretation results indicates the presence or severity of the respective symptom in the medical image; and obtaining a disease interpretation result (36, 46) of the disease based on the N feature maps (Fm_1 to Fm_N) through the first classifier, wherein the disease interpretation result indicates the severity of the disease; wherein the convolutional neural network model (M1, M2, M3) further comprises a symptom feature extraction part (48_1 to 48_8), and wherein the step of obtaining N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8) of the N symptoms of the disease based on the N feature maps (Fm_1 to Fm_N) through the N second classifiers (33_1 to 33_N) comprises: inputting one of the N feature maps (Fm_1 to Fm_N) to the symptom feature extraction part (48_1 to 48_8) for obtaining at least one symptom feature map; and inputting the at least one symptom feature map to one of the N second classifiers (33_1 to 33_N) so that the one of the N second classifiers (33_1 to 33_N) outputs one of the N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8). inputting one of the N feature maps (Fm_1 to Fm_N) to the symptom feature extraction part (48_1 to 48_8) for obtaining at least one symptom feature map; and inputting the at least one symptom feature map to one of the N second classifiers (33_1 to 33_N) so that the one of the N second classifiers (33_1 to 33_N) outputs one of the N symptom interpretation results (34_1 to 34_N, 44_1 to 44_8).