## 3 Results
### Deep Learning Models
#### 3.3.1 U-Net and Derived Models
The visual choice from the ensemble allowed for a higher score of 0.79 \(\pm\) 0.16, while strategies such as majority voting or averaging yielded lower performances (approximately 0.75 \(\pm\) 0.22), indicating that human intervention is still required to achieve optimal segmentation. Another frontier involves the enhancement of the U-Net architecture proposed by [PERSON] et al. [40], combining a recurrent attention module with a dense residual module based on dilated convolution, to segment malignant breast lesions in T2-weighted DCE-MRI. In this case, a two-step approach is used, with a preprocessing stage where a 2D U-Net is trained to segment the whole breast area, followed by a second stage in which lesion segmentation is performed only within the ROI. To effectively handle imbalanced datasets, a Tversky loss function was used. Moreover, to improve model accuracy, exhaustive data augmentation was applied, including scaling, mirroring, and elastic deformations. The network processes individual 2D slices, and a final 3D reconstruction is obtained by merging all the 2D output slices. The model was trained on 128 subjects and tested on 32 cases, obtaining a mean Dice score of 0.78. The enhanced model outperformed other pre-existing models, including U-Net, attention U-Net, residual U-Net, dense U-Net, and V-Net, which all scored no higher than 0.76 on the same dataset. In addition, [PERSON] et al. [41] also investigated breast lesion segmentation on DCE data, using an improved version of U-Net that incorporates not only an attention module but also an edge feature extraction module to accurately capture edge details and small lesions. In this case, preprocessing consisted of cropping the lesion layer, normalisation, and applying data augmentation techniques such as rotation, scaling, flipping, and shifting. The model uses a single input channel, taking the most significant phase of the DCE-MRI to identify the lesion as suggested by radiologists. The dataset consisted of 1110 training cases, 476 internal testing cases, and another group of 174 cases from other centres for external testing. A comparison between the model's results and manual segmentation achieved a mean Dice score of about 0.83 and 0.82, respectively, in the internal and external test sets. The improved U-Net also outperformed other state-of-the-art methods, such as SegNet, U-Net++, U-Net, Att-U-Net, CS-Net, and Multistage Hierarchical Learning, which achieved Dice scores in the range of 0.69 to 0.77. Moreover, the segmentations were used to extract features for discriminating axillary lymph node status, obtaining promising results that reinforced the reliability of the model. Other types of enhanced U-Net have been used by [PERSON] et al. [42], who aimed to segment BIRADS 4 breast lesions from DCE-MRI and DWI. In particular, to segment the DCE-MRI, a 2D V-Net was used, employing data from three phases: the pre-contrast, the initial uptake, and the phase after an 8-10 min delay. To segment the DWI, an Attention U-Net was preferred. In both cases, the same combined loss was used, consisting of a sum of BCE and Dice loss. Both models were trained, tested, and validated on a dataset of 2823 subjects. Performance was assessed by comparing automatic segmentation with manual radiologist ground truth, obtaining a mean Dice score of 0.86 for the V-Net on the DCE-MRI and 0.829 for the Attention U-Net on the DWI. Moreover, both automatic segmentation tools were used to provide a baseline for lesion classification, reaching high AUC values, suggesting that the models produced reliable segmentations. Despite focusing more on the classification of molecular subtypes rather than on segmentation, the work of [PERSON] et al. [43] also provides some results in terms of segmentation performance using derivations of U-Net. In particular, they aim to segment malignant breast lesions from DCE-MRI using a 2D-DenseU-Net based on the DenseNet-121 architecture. The segmentation operates on 2D slices.