## 2 Related Work  
Breast cancer patients can benefit from the use of nuclear medicine imaging in two ways: locating and categorizing axillary lymph nodes, and performing distant staging [22]. Recent advances in the digitalization of imaging processes and artificial intelligence have opened the horizon for the potential application of DL in breast cancer imaging and mammography [26; 27; 28]. DL has recently found application in various fields, such as lesion identification and segmentation, image reconstruction and generation, risk evaluation for cancer, and forecasting therapeutic outcomes [29; 30; 31].  
Numerous research endeavors have examined the efficacy of convolutional neural networks (CNN) in detecting and categorizing breast cancer [32; 33], with the U-Net architecture being a prominent CNN model used for medical imaging, specifically for image segmentation tasks [34]. The U-Net comprises an encoder that extracts features from the input image and a decoder that generates a segmentation mask of equal size to the input image.  
More recently, the U-Net architecture has been expanded to 3D for volumetric medical imaging data. Transfer learning is a widely adopted approach in deep learning for medical image analysis [35]. Several studies have applied DL and transfer learning techniques to breast cancer imaging using 3D U-Net models [36; 37; 38]. [PERSON] et al. [39] and [PERSON] et al. [40] utilized transfer learning with a 3D U-Net model to enhance the segmentation of breast tumors in MRI scans. Their approach involved training the model on a sizable dataset of abdominal CT scans and fine-tuning it on a smaller dataset of breast MRI scans. In another investigation conducted by [PERSON] et al. [34], the use of transfer learning with a 3D U-Net model yielded a higher accuracy and specificity in classifying breast cancer histopathological images compared to traditional ML techniques.  
Similarly, [PERSON] et al. [22] used the same approach of using a 3D U-Net model with transfer learning to classify breast cancer subtypes using diffusion-weighted MRI scans and achieved a higher accuracy and sensitivity compared to traditional approaches. [PERSON] et al. [21] applied the same approach for the segmentation of breast tumors in ultrasound images with higher accuracy and the Dice similarity coefficient (a measure of segmentation accuracy). Conclusively, the studies discussed indicate that 3D U-Net models with transfer learning have the potential to improve the accuracy of breast cancer diagnosis across various medical imaging modalities. By leveraging these techniques, researchers may be able to develop more accurate and efficient methods for detecting and diagnosing breast cancer, ultimately leading to improved patient outcomes. Table 1 shows the details of a few studies which employed this state-of-the-art technique of using a 3D U-Net model for computer-aided diagnosis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Datasets** & **Techniques** & **Results** \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [23] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Cancer Imaging Archive \\ (TCIA) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Fine-tuning \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Improved ductal carcinoma \\ classification using fine-tuned 3D \\ U-NET model. \\ \end{tabular} } \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of 3D-U-Net-model-related work.