## 3 Results
### Deep Learning Models
#### 3.3.1 U-Net and Derived Models
To improve training and alleviate overfitting, data augmentation including scaling and random mirroring was performed. The dataset consists of 82 cases, of which 66 are used for training and 16 for testing, achieving a mean Dice score of approximately 0.720 compared to the radiologist's manual ground truth. Similarly, [PERSON] et al. [44] used the nnU-Net model to segment both malignant and benign breast lesions. They used a single image as input to the network, specifically the first post-contrast image of the DCE series. Preprocessing involved two steps: splitting the dataset into right and left breasts to augment data, reduce overfitting, and increase the relative size of the lesion for easier detection; and a second stage of intensity normalisation. The model was trained and tested on 196 and 50 cases, respectively. Performance was evaluated with respect to manual ground truth provided by radiologists, achieving a mean Dice score of approximately 0.831, which is very close to the inter-reader variability assessed at 0.830, demonstrating overall good performance. Moreover, the segmentation served as a baseline for radiomic feature extraction and the development of a classifier with good predictive values, suggesting the efficacy of the segmentation. [PERSON] et al. [45] also used nnU-Net to perform malignant breast lesion segmentation from 3D DCE-MRI data. In particular, they proposed a two-stage approach, with the first stage focused on mammary gland segmentation and the second stage on segmenting the lesion within the mammary ROI. Both stages exploit the same nnU-Net architecture, capable of handling 3D data. The first stage is essential to reduce irrelevant information with similar enhancement, such as the heart, effectively reducing false positives. To include multicentric data, a histogram matching technique was employed, which served both to increase the model's transferability to other datasets and to provide data augmentation,preventing overfitting. To capture both global and local information, the loss function combined cross-entropy and Dice terms. To address computational costs, a patch-based approach with overlapping patches was employed, where edge pixels were assigned different weights, resulting in a fused final segmentation. As a postprocessing step, only the largest connected contour was retained, effectively reducing the impact of false positives by discarding smaller fragments. The dataset consisted of 361 breast cancer samples from two hospitals equipped with different scanners, using 246 samples from the first institution for training and 115 samples from the second for testing. The model's performance was evaluated in two scenarios: without histogram matching, yielding a mean Dice score of approximately 0.78, and with histogram matching, which improved the score to 0.83. This demonstrated the model's strong generalisation capabilities. Moreover, the automatic segmentations served as a reference to extract radiomic features and build a predictive model for neoadjuvant response of axillary lymph nodes, showing promising results and further enhancing the model's reliability. [PERSON] et al. [46] focused on comparing the performance of 2D and 3D U-Net models for general malignant mass and non-mass breast lesions on DCE-MRI, with respect to 3D FCM and 2D manual segmentations produced by radiologists. To obtain 3D segmentations from the 2D U-Net, individual 2D slices were stacked to create a 3D volume. The dataset consisted of 689 patients, with a total of 994 unique lesions of varying sizes and types. Model performance was evaluated using 5-fold CV. An initial comparison between FCM and U-Net resulted in a median Dice score of 0.78 (IQR: 0.774, 0.787) for the 2D U-Net and 0.721 (IQR: 0.710, 0.732) for the 3D U-Net. A second comparison utilised a subset of 71 2D segmentations provided by radiologists, corresponding to the central slice of each lesion. In this case, the 2D U-Net achieved a median Dice score of 0.864 (IQR: 0.845, 0.889), while the 3D U-Net reached a score of 0.802 (IQR: 0.766, 0.834).