Weakness • The reviewer thinks the authors need to elaborate how the output labels are defined for density assessment. In section 3. Datasets, it seems the authors gives confusing definitions of density and BIRADS findings like “we categorized BI-RADS density scores into two separate categories: BI-RADS 2 and 3 as benign and BI-RADS 5 and 6 as malignant”. There is no description about what “Density A”, “Density B”, “Density C”, and “Density D” mean. Also, as the reviewer knows, benign or malignant classification can be confirmed with biopsy results not BIRADS scores. Even though the reviewer is not familiar with the two public datasets, the reviewer thinks the datasets should have biopsy information to annotate lesions whether malignant or benign. • As a preprocessing step, the authors segmented and removed the region of the pectoral muscle from MLO views. However, the authors did not explain how the segmentation model was developed (they just mentioned employed the prior work) and the review has a concern that important features can be removed from this preprocessing step. It might be useful to compare model performance using MLO views with and without this preprocessing step to confirm the benefit of this pectoral muscle removal. • How did you calculate precision/recall/F1-score for 4-class classification of breast density? Also, for breast cancer detection, researchers usually report AUC with sensitivity and specificity at different operating points to compare model performance. It might be more informative to provide AUC results for comparisons. • The reviewer thinks comparison of their proposed approach with the single-view result is unfair. This is because information that multi views contain is 4x larger than the one that the single view has. So, to demonstrate the benefit of using the proposed fusion strategy, they need to report performance of multi-view results with simple fusion approach like the average/maximum of 4 view scores, or max over mean values of each breast. • Are the results reported in this study based on patient/study level? How did you calculate performance when using single views? Did you assume that each study has only one view? • What fusion strategy was used for results in Table 2? Are these results based on image level?
• How did you calculate precision/recall/F1-score for 4-class classification of breast density? Also, for breast cancer detection, researchers usually report AUC with sensitivity and specificity at different operating points to compare model performance. It might be more informative to provide AUC results for comparisons.