## 3 Proposed Method
### Two-View-Based Deep Model
#### 3.3.6 Multi-Branch Classification  
Our system classifies input ROIs into three distinct groups: density, severity level, and abnormality type. To enhance feature representation and task-specific learning, we integrated three branches of fully connected layers at the end of the fusion model. Each branch in the multi-branch architecture focuses on learning features specific to a particular task, allowing the model to extract more effective task-specific features. In the post-fusion layer, we apply three parallel FC layers dedicated to density, severity level, and abnormality type, enabling distinct learning for each task. The architecture includes a series of FC layers leading to a classification layer, with configurations determined via an ablation study. We use FC 256, FC 128, and dense layers with ReLU activation, batch normalization, and then the classification layer. This setup helps in learning task-specific features for efficient classification. The final classification layer makes predictions for the three groups. Density classification uses four output neurons for BI-RADS classes, while abnormality type and severity level classifications use two output neurons each for classifying into mass or calcification and benign or malignant, respectively. We employ the softmax activation function in each group's output layer for these tasks. This function transforms the network's output into class-specific probabilities, aiding in accurate class determination. The choice of softmax is guided by our multi-label classification needs and the nature of the tasks. Figure 5 illustrates the multi-branch classification block. In this block, we employ a series of mathematical equations to map the input data to the output, with the overarching goal of achieving multi-label classification. Specifically, we utilize these equations to model and predict probability distributions for three distinct branches: density (denoted as \(d\)), severity level/pathology (denoted as \(p\)), and abnormality type/findings (denoted as \(n\)). The equations for each branch are presented as follows. For a given branch,  
\[b\ (b\in\{d,p,n\}):\ P^{b}=Softmax\ \left(f_{3}\ ^{b}\ o\ f_{2}\ ^{b}\ o\ f_{1}^{b}\ \ (x_{Fused})\right) \tag{6}\]  
In this equation, \(P^{b}\) represents the probability distribution specific to branch \(b\), and \(\{f_{3}\ ^{b}\,\ f_{2}\ ^{b}\,\ f_{1}^{b}\}\) denote the functions modeled with fully connected layers (FC1, FC2, and FC3) tailored to each branch. The output is a probability vector \(p^{b}\), and the predicted class label \(b^{b}\) is determined as \(\max\max_{1\leq i\leq b^{b}}p_{i}^{b}\), where \(k^{b}\) is the number of classes associated with branch \(b\). Each function \(f_{i}\ ^{b}\) follows a consistent structure, comprising a fully connected layer, batch normalization, ReLU activation, and dropout. The number of neurons and layer-specific parameters may vary across branches. Additionally, in each branch, we implement  
Figure 5: Multi-branch classification block. It takes a fused feature and passes it to three parallel task blocks. It presents a multi-branch neural network architecture with a unified task block structure replicated across three tasks: density, pathology, and severity level categories. The left side enumerates sequential task blocks, indicative of a deep and comprehensive processing approach. On the right is a breakdown of the task block. The output layer employs a softmax function, tailored with four classes for the density task and two classes for both pathology and severity levels. a two-stage projection on the fusion features, initially reducing their dimension from \(D\) to \(D_{1}\), and then further compressing them to \(D_{2}\), which are variables determined according to the specific branch's requirements. In each branch, we employ a two-stage projection on the fusion features, initially reducing their dimension from 1536 to 256, and then compressing them to a 128-dimensional space.