## 4 Discussion  
Across the last years, diverse ML models were used to deal with the challenge of breast lesion segmentation on MRI data. The differences in sample size, inclusion criteria, and methodology make it difficult to compare results and performances across the diverse proposed methods. A first essential criticality regards the sample size, that in the majority of cases is reduced. The paucity of publicly available datasets with labels restricts very often the analysis to private local samples from a single centre, using a single scanner and only a limited variety of lesions. Differences in scanners and acquisition parameters could be challenging, and it is difficult to forecast the effects on model performances without having external validation sets. In some fortunate cases where multicentric and multiscanner datasets were available, solutions such as data augmentation based on histogram matching to data from other centres proved to be highly effective, as pointed out by the study of [PERSON] et al. [45]. Another possible issue is the fact that most of the studies with larger samples use subjects of Asian populations exclusively, thus there is the need to include more heterogeneous data also from other populations, as suggested by [PERSON] et al. [51]. Moreover, in general, reduced sample sizes fail to represent different parenchymal compositions, often leading to false positive detections. Private datasets tend to be smaller in proportion to the local population around the centre; moreover, data labelling is often a burden for radiologists, who might handle more urgent tasks, dilating times to collect large enough samples. Moreover one has to consider that the use of ML in medical imaging plays a critical role in improving diagnostic and prognostic stages. However, the risk of bias must always be taken into account. The potential for mitigating cognitive bias in the interpretation of images might be compromised by unbalanced training datasets, suboptimal architecture design, and incorrect application of models [53]. The use of imbalanced training datasets has been shown to lead to poor performance when applied to heterogeneous populations, whereas models trained on balanced demographic groups achieve better performance. It is, therefore, essential to investigate further and work toward building commonly shared, balanced datasets for training, to create equitable models and reduce potential sources of bias. To achieve unbiased models, improvements in terms of publicly available heterogeneous datasets are also mandatory. Before designing a study, it could be useful to have common training or testing datasets based on a global population to reduce bias. For this purpose, some datasets are already publicly available, such as the BreastDM database from [54], or data provided by The Cancer Imaging Archive, which includes various imaging datasets like Breast MRI QIN-BREAST [55] and QIN Breast DCE-MRI [56]. In the former years, unsupervised clustering techniques and simpler supervised models were explored, thus the need for large datasets was not evident. However, the preference for highly performant deep learning approaches heavily demands larger and larger datasets in order to train effectively the more complex models without overfitting. The predominant approach is, in fact, the U-Net, that like all supervised methods requires labels. To reduce both labelling time and bias, and to address the lack of local annotations provided by experts, more attention should be directed toward the integration of weakly supervised models utilising minimally labelled slices in 3D lesions, such as deep multiple instance learning [57]. Alternatively, unsupervised networks, such as Generative Adversarial Networks (GANs) [58], or generalisations of the U-Net architecture designed to work in a completely unsupervised manner, like the W-Net [59], could be explored. Additionally, hybrid frameworks that combine the advantages of both supervised and unsupervised approaches present another promising avenue for research.