## 3 Results
### Deep Learning Models
#### 3.3.1 U-Net and Derived Models  
U-Net's typical symmetrical encoder-decoder architecture dominates the landscape of deep learning segmentation for medical imaging. Initial studies, such as the one conducted by [PERSON] et al. [36], utilised the original 2D model, including partial information from the DCE-series, using only two input channels. However, the intrinsic challenge of segmenting breast lesions, especially malignant ones, calls for increasing customisation of the network architecture, including generalisation to 3D datasets to capture small lesions and the introduction of customised attention modules. Moreover, several preprocessing steps (generation of a breast ROI, cropping, normalisation, etc.) were commonly used to reduce misleading contributions, together with data augmentation techniques to prevent overfitting and the use of modified loss functions (e.g., introduction of a Dice contribution to binary cross-entropy) to improve the model's performance. Attempts to use more informative time points exploiting the full TIC information have been made by [PERSON] et al. [37], who used three time points, and by [PERSON] et al. [38] and [PERSON] et al. [39], who employed ensembles of 3D U-Net models, each with diverse combinations of time points as input. [PERSON] et al. [40] introduced a recurrent attention module, while [PERSON] et al. [41] incorporated an edge feature extraction module to better capture edge details and small tumours. Attention modules were also employed by [PERSON] et al. [42]. Further modifications to the architecture, such as DenseUNet and nnU-Net, were effectively explored by [PERSON] et al. [43], [PERSON] et al. [44], and again by [PERSON] et al. [45]. Finally, comparisons between general 2D and 3D U-Net approaches and the use of specific time points were explored by [PERSON] et al. [46], reflecting the difficult evaluation and selection of the ideal model. One of the first applications of the U-Net architecture for segmenting malignant and benign breast lesions was conducted by [PERSON] et al. [36]. They used a U-Net architecture with two separate input channels for the pre-contrast and the first post-contrast images of a DCE-MRI series acquired in the sagittal plane. This choice reflected the approach typically used by radiologists when performing manual segmentation. The network was fed with individual 2D slices containing lesions and was trained using a binary cross-entropy (BCE) loss function. To improve model performance, data augmentation techniques, including small random rotations, were also applied. The model was trained on 263 subjects and tested on 54 subjects, using reference segmentations from three different radiologists. When the first radiologist's segmentations were used to train the model, a mean Dice score of \(0.71\pm 0.16\) was obtained with respect to their own segmentations, while Dice scores of \(0.61\pm 0.17\) and \(0.67\pm 0.18\) were achieved when compared with the ground truth from two other radiologists. The procedure was repeated using segmentations from a second radiologist for training, yielding slightly lower Dice scores of \(0.67\pm 0.20\), \(0.64\pm 0.22\), and \(0.61\pm 0.18\) for comparisons with different operators. These results were comparable to the mean inter-operator variability, which ranged between 0.61 and 0.68. The effectiveness of the obtained segmentations was also assessed in terms of predictive capability. The segmentations produced by the model were used as a baseline for feature extraction to predict lymph node metastases, achieving area under the curve (AUC) values comparable to those obtained using manually generated segmentations. This suggests that the U-Net captures relevant information and exhibits predictive power comparable to radiologists for this specific task. An improvement of the two-input channel U-Net comes from the work of [PERSON] et al. [37], who proposed using a three-time-point framework to segment breast lesions from DCE-MRI.