## 3 Results
### Deep Learning Models
#### 3.3.2 Alternative Approaches  
In spite of the predominant use of U-Net, other deep learning approaches have also been explored. For instance, bounding box-based detection methods, which focus on identifying regions of interest before segmentation and primarily utilise ResNet as a backbone, were proposed by [PERSON] et al. [47] and [PERSON] et al. [48]. A subsequent approach investigated by [PERSON] et al. [49] utilised multiple decoder streams to identify lesion locations, refine boundaries, and produce complete segmentations. In addition, there are also multi-encoder architectures capable of handling multimodal data, such as the one developed by [PERSON] et al. [51]. Moving away from traditional CNN, the use of neural ordinary differential equations has also been investigated, as demonstrated by [PERSON] et al. [50], who proposed an attention-based U-Node. In particular, the work of [PERSON] et al. [47] aimed to segment malignant mass lesions using DCE-MRI. The model was based on a custom derivation of a mask region-based CNN, employing the pretrained ResNet-101 as its backbone. The main features of the system involve the parallel processes of region proposal, lesion detection, and segmentation. The architecture has three input channels: one for the pre-contrast image, useful for identifying the chest area, and two for subtraction images, respectively representing the diseased breast and the contralateral normal one. On each 2D slice, several predefined bounding boxes are placed to identify abnormalities, which are then ranked and classified as containing lesions or not. Finally, a segmentation network produces the lesion segmentation. The model was trained with 241 cases of fat-saturated images, while testing was performed on 98 subjects with non-fat-saturated images. Automatic segmentation was compared with FCM ground truth. Performance on the training set was estimated via 10-fold CV, obtaining a mean Dice score of 0.82, ranging from 0.64 to 0.97. On the test set, a mean Dice score of 0.79 was reported, with values ranging from 0.31 to 0.97. Despite good performance in some cases, small lesions were not detected, and strong parenchymal enhancement led to false positives. Nevertheless, the model can be regarded as robust, performing well on both fat-saturated and non-fat-saturated images. [PERSON] et al. [48] applied an alternative hierarchical approach to 2D breast tumour segmentation on T1w with contrast (T1C) MRI. In the first stage, the breast region is segmented using a simple U-Net, and within this breast area, the second stage of lesion segmentation is applied. They developed a lesion morphology-aware network based on a new hybrid loss function (revised binary sigmoid + Dice) that maximises inter-class distance, reducing both false positives and false negatives. The customised network utilises adaptive deformable convolutions that interact with spatial-wise and channel-wise attention modules, where the receptive field is adjusted based on lesion morphology through a learnable dilation parameter. The model is based on the structure of DeepLab V3+, with ResNet-101 serving as the backbone for the encoder. A total of 413 subjects were used for training, 59 for validation, and 118 for testing. The network was also able to detect tumours missed by radiologists and produce smoother boundaries. Performance assessed against manually delineated tumours achieved a mean Dice score of 0.87 \(\pm\) 0.14. Other models, including U-Net, DeepLab V3+, and hierarchical CNN, were also compared, with lower Dice scores (mean Dice scores ranging from 0.83 to 0.85). [PERSON] et al. [49] proposed a three-stream subnet for BIRADS 4 lesion segmentation on breast MRI. The segmentation network includes three decoders that simultaneously work to locate the lesion, identify boundaries, and produce the final segmentation. Unlike previous approaches, ResNet-34 is used as a building block, with a location stream focusing on extracting features only from the lesion area, thereby preventing the loss of small lesions.