## 3 Results
### Early Unsupervised Models
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
\begin{tabular}{} \end{tabular} & attention U-Node 3D & Yes & 3-fold CV & 3-fold CV & 3-fold CV \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In particular, the most significant work in terms of sample size was performed by [PERSON] et al. [32]. Their work aimed to segment both benign and malignant breast lesions, utilising multimodal clinical breast data (DCE-MRI, T1w, T2w) and implementing mean-shift clustering followed by the creation of a region adjacency graph on which graph cuts are applied. The dataset consisted of a total of 128 subjects, that were divided into a training set (35 cases) and two test sets with data from different scanners (respectively with 93 and 9 lesions), reserving the training set for tuning model parameters. The input image consisted of vector-valued voxels obtained by stacking values from each modality. The need to minimise false positives in the chest cavity and reduce computational times prompted the inclusion of a preliminary step for segmenting the breast tissue. Meanwhile clustering is then applied on the vector-valued voxels within the restricted breast ROI, grouping similar voxels in the multidimensional feature space. Once the clusters have been obtained, a region adjacency graph over them is calculated, blood vessels are identified, and only the suspicious tissues are segmented by applying graph cuts employing a dissimilarity measure evaluating the difference of pre- and post-contrast signals. Finally, lesion candidates with too small a size are removed, and the remaining lesions are ranked based on the relative enhancement. Performances were assessed against the manual segmentation of an experienced radiographer and obtained a median DSC of 0.76 (IQR 0.17) and 0.75 (IQR 0.16), respectively, on the first and second test sets. Overall, their pipeline achieved a fully automatic segmentation, exploiting the potential of multimodal data and reaching comparably robust results on data from different scanners and vendors. Another approach to segment breast lesions, in particular for invasive ductal carcinomas, was proposed by [PERSON] et al. [31]. They proposed to exploit the full DCE-MRI information by using time-series analysis and FCM clustering. Relevant features can be extracted from the TIC, creating an observation vector that was modelled with a linear dynamic system. The clustering was set with two classes, allowing discrimination between tumour and healthy voxels on a voxel-by-voxel basis, producing a final map with tumour delineation. The procedure was tested on a dataset with 24 patients, comparing automatic segmentation with the manual segmentation of both a radiologist and preexisting commercial software, obtaining, respectively, a mean dice score of 0.77 and 0.72. In addition, [PERSON] et al. [33] investigated another clustering method exploiting the TIC to segment generic (both malignant and benign) breast lesions from DCE-MRI. Their line of action involved the use of all the available time-points \(T\) to create high-dimensional images of size \(n\times T\), where \(n\) is the number of pixels. To reduce computational costs while preserving information, the image dimensionality was subsequently reduced to a 3-dimensional space by using the non-linear method of Laplacian Eigenmaps, preserving TIC information and manifold structure. Finally, k-means clustering was applied to the 3D enhanced dataset, where the lesion becomes more evident, resulting in the final segmentation. The whole procedure was tested on 36 clinical cases, comparing automatic segmentation with the radiologist's gold standard segmentation, achieving overall high specificity (\(0.98\pm 0.01\)) and sensitivity (\(0.90\pm 0.04\)).