## 2 Materials and Methods
### Deep Biomarker Extraction  
The feature extraction pipeline followed a two-stage approach combining anatomical structure preservation with deep representation learning. First, each OCT image underwent preprocessing to standardize intensity distributions and enhance pathological markers:  
\[I_{norm}=\frac{I_{raw}-\mu_{ImageNet}}{\sigma_{ImageNet}}\circ M_{roi},\mu=[0.485,0.456,0.406],\sigma=[0.229,0.224,0.225] \tag{4}\]  
where \(M_{roi}\) denotes a retinal layer segmentation mask preserving diagnostically relevant regions. Note that the constants \(\mu_{ImageNet}\) = [0.485, 0.456, 0.406] and \(\sigma_{ImageNet}\) = [0.229, 0.224, 0.225] are standard normalization values derived from the ImageNet dataset, ensuring compatibility with the pretrained VGG16 network. The VGG16 architecture \(F_{\theta}:\mathbb{R}^{224\times 224\times 3}\rightarrow\mathbb{R}^{512}\) pretrained on ImageNet served as our feature extractor. Global Average Pooling distilled spatial biomarker information:  
\[\phi^{(k)}(I)=\frac{1}{\text{Area}(\Omega^{(k)}_{bleb})}\iint_{\Omega^{(k)}_{bleb }}F^{(k)}_{\theta}(x,y,z)\,d\Omega,\quad k=1,\cdots,512 \tag{5}\]  
with \(\Omega^{(k)}_{bleb}\) representing pathological fluid regions in choroidal neovascularization. Although an integral is used in Equation (5) to denote the operation over the region \(\Omega^{(k)}_{bleb}\), in the code itself this is calculated as a discrete summation (i.e., an average pooling) over the corresponding pixel activations. In this way, the notation serves as an abstraction for the pooling operation performed on the VGG16 feature maps. The feature extraction process was implemented as follows:  
\[\mathbf{x}_{i}=\Phi(I_{i})=[(\phi^{(1)}(I_{i}),\phi^{(2)}(I_{i}),\cdots,\phi^{ (512)}(I_{i})]^{T} \tag{6}\]  
where each \(\mathbf{x}_{i}\in\mathbb{R}^{512}\) represents the embedded representation of OCT image \(I_{i}\) in the biomarker feature space. The feature extraction utilized transfer learning through a pre-trained VGG16 model with weights initialized from ImageNet training. The avgpool layer output produced a compact 512-dimensional representation capturing hierarchical features across different retinal anatomical structures. The embedded representation prioritized features from convolutional layers sensitive to the following:  
* Retinal layer boundary disruptions indicative of pathology. * Subretinal fluid accumulations in exudative conditions. * Drusen deposits characteristic of early AMD. * Retinal thinning and structural deformation patterns. Each extracted feature vector underwent statistical normalization to ensure numerical stability during downstream modeling:  
\[\hat{\mathbf{x}}_{i}=\frac{\mathbf{x}_{i}-\mu_{\mathbf{x}}}{\sigma_{\mathbf{x }}}\quad\text{where}\quad\mu_{\mathbf{x}}=\frac{1}{N_{train}}\sum_{i=1}^{N_{ train}}\mathbf{x}_{i}\quad\text{and}\quad\sigma_{\mathbf{x}}^{2}=\frac{1}{N_{ train}}\sum_{i=1}^{N_{train}}(\mathbf{x}_{i}-\mu_{\mathbf{x}})^{2} \tag{7}\]  
VGG16 was selected as our feature extractor following evaluation against alternative CNN architectures. VGG16's pretrained convolutional layers offer specific advantages for OCT image analysis: (1) its hierarchical feature organization effectively captures the stratified nature of retinal layers, with earlier layers detecting boundaries and later layers identifying pathological patterns; (2) its uniform \(3\times 3\) convolutional filters demonstrate superior sensitivity to fine-grained texture variations in fluid accumulations and retinal layer disruptions; (3) its feature generalization capabilities enable robust performance across heterogeneous OCT datasets despite equipment variations; and (4) its transfer learning efficiency mitigates the limited availability of pathology-specific training data.