Deep Neural Networks-based Malignant Breast Lesions Detection and Segmentation from Mammography
Moghadaseh Khaleghi Bizaki, Alireza Vafaei Sadr, Mehdi Amini, Nahid Nafissi, Isaac Shiri, Habib Zaidi, Reza Reiazi · 2022
Breast cancer has the highest incidence among all cancer types for women globally. Convolutional Neural Networks (CNN) is the most widely used models for computer-aided breast cancer detection in mammograms. This paper used CNN-based architecture for malignant lesion detection and segmentation in mammography images using the public CBIS-DDSM and an in-house (IMI) dataset. The CBIS-DDSM consists of 1000 images, and the IMI consists of 295 images. The CBIS-DDSM was randomly split into 80/10/10% of training/validation/ testing datasets. In this study, we applied the U-Net model and devised two setups: first, train and test the model with the CBIS-DDSM, and second, use transfer learning in which the pre-trained model on the CBIS-DDSM dataset is fine-tuned by the IMI train set and tested on the IMI test set. Evaluation metrics for calculating the similarity include the Dice coefficient, but we also reported the Intersection-Over-Union index. The Free-Response receiver operating characteristic curve (FROC) was chosen to evaluate the detection performance as an evaluation criterion. The dice and IOU values of the first setup were 0.98±0.006 and 0.97±003, respectively, which indicates that the predicted lesions are highly similar to ground-truth lesions. The FROC curve of setup1 shows that the model could detect lesions with the true positive rate of 0.99±0.17 at the false positives per image of 2.7. In setup 2, the pre-trained model with the CBISM-DDSM dataset tuned with the IMI achieved the dice and IOU of 0.92±0.0 and 0.98±0.008, respectively, on the IMI test set. Also, the model detected lesions with a true positive rate of 0.98±0.20 at the false positives per image of 3.3. Our study showed the potential of the Transfer learning techniques to guarantee the reliable performance of publicly developed models by tuning them with a low number of in-house patients.