## 3 Results
### Deep Learning Models
#### 3.3.2 Alternative Approaches
Additionally, an upsampling layer aids in refining boundaries. Finally, the segmentation decoder, based on U-Net, incorporates multiscale receptive fields to adapt to lesions of different sizes. The dataset consists of 248 subjects, with 70% used for training and the remaining 30% reserved for testing. Automatic segmentation was compared to radiologists' ground truth, achieving a mean Dice score of 0.70 on the test data. Performance was also assessed for other models, which yielded lower results, such as CE-Net (Dice 0.67), Shape Attentive U-Net (Dice 0.66), U-Net+ with data augmentation (Dice 0.65), U-Net+ (Dice 0.63), AttU-Net (Dice 0.63), U-Net (Dice 0.61), and W-Net (Dice 0.60). The importance of using a three-stream approach was also investigated, showing slightly lower performance when stages were removed (mean Dice of 0.69 when only the segmentation stream was used). Moreover, the segmentations were used as the basis for a further classification subnet, enabling discrimination between benign and malignant lesions. The challenge of malignant tumour segmentation was addressed by [PERSON] et al. [51], who employed multiparametric imaging using DCE-MRI as the primary modality, complemented by T2w and ADC data. A first stage was reserved for the generation of synthetic T2w and ADC data from MRI using a residual U-Net with \(L_{1}\) loss. Real and synthetic images were compared and used to generate modality-specific attention maps. Segmentations were produced using a multiencoder architecture with latent feature fusion, enhanced by modality-specific attention modules, modality trust gating modules, and deep supervision modules with dice losses. Performances were assessed with 4-fold CV on two separate datasets, comprising 600 and 493 subjects, respectively. In each case, 60% of the data was used for training, 20% for validation, and 20% for testing. The model achieved a Dice score of \(0.8903\pm 0.0195\) on the first dataset and \(0.8445\pm 0.0127\) on the second. The proposed model significantly outperformed other multimodality methods in both cases, demonstrating overall efficacy. In addition, the automatic segmentation of breast lesions was used to train a model for predicting NAC response, achieving a predictive accuracy comparable to that of manually segmented tumours, underscoring the reliability of the proposed framework. A different approach based on neural ordinary differential equations (ODE) was investigated by [PERSON] et al. [50]. Their work relies on the fact that deep neural networks can represent a discretisation of a continuous dynamical system, thus deep learning problems can also be formulated in terms of differential equations that can be solved using traditional techniques. They combined the concepts of neural ODE with the U-Net encoder-decoder structure to segment malignant and benign breast lesions in both MRI and ultrasound (US) data. The use of ODE blocks in place of standard convolutional blocks allows for a reduction in the number of parameters, thereby mitigating the risk of overfitting. To improve performance, the Convolutional Block Attention Module (CBAM) was implemented, incorporating channel and spatial attention submodules. Additionally, a combined loss function, including cross-entropy and Dice terms, was employed. The model demonstrated generalisation for both 2D and 3D data. Performance on 2D DCE-MRI was assessed using a private dataset of 150 patients with 3-fold CV, yielding a mean Dice score of 0.70. Similar results were obtained on a 3D dataset of 10 patients. The Attention-U-Node was compared with other 2D and 3D models, consistently achieving superior performance on the given datasets. Furthermore, the same network was successfully trained and tested on multiple US datasets, demonstrating good adaptability to different imaging modalities. A direct comparison of results from different models and approaches is not straightforward, as no common public dataset was used to evaluate performance on a standard basis without introducing bias.