## 3 Methods
### Experimental Details
#### 3.5.2 Dataset and Preprocessing  
In this paper, we utilized the PH\({}^{2}\) dataset alongside the ISIC 2017 and ISIC 2018 datasets from the ISBI challenge for model training and validation. The PH\({}^{2}\) dataset comprises 200 dermoscopic images, which were divided into 140 training samples, 20 validation samples, and 40 test samples. The ISIC 2017 dataset consists of 2000 original images with corresponding annotations, supplemented by 150 validation samples and 600 test samples. The ISIC 2018 dataset contains 2596 images and their annotations, supplemented by 200 validation samples and 596 test samples. The detailed composition of these datasets is summarized in Table 1. Collectively, these datasets cover three primary types of skin lesions, melanoma, seborrheic keratosis, and nevus, which offer both strong representativeness and significant challenges for segmentation tasks. As illustrated in Figure 8, images from the ISIC 2017 test set exhibit lesions on raw dermoscopic images. Notably, the presence of noise artifacts such as hair and bubbles is evident, which increases the complexity of lesion segmentation and poses challenges for practical applications.  
The dataset utilized in this study exhibits significant challenges for deep learning-based feature extraction, including image noise, blurred boundaries, heterogeneous skin tones, hair occlusion, capillary interference, and diverse lesion morphologies. Given the limited size of publicly available dermatological datasets and the prohibitive cost of manual annotation, we implemented an extensive data augmentation pipeline incorporating both geometric transformations and photometric variations. This approach effectively expanded the training dataset while preserving pathological features, as validated through quantitative performance improvements in subsequent experiments.  
To address the challenges posed by varying image sizes and significant resolution discrepancies in skin lesion datasets, this study implements a comprehensive data normalization and augmentation strategy. The specific details of the dataset are shown in Table 1. Initially, all lesion images are rescaled to a uniform resolution of 256 \(\times\) 256 pixels to eliminate scale variability. Furthermore, to mitigate the limitation of insufficient sample sizes in publicly available datasets, a multidimensional data augmentation approach is employed. This includes geometric transformations such as rotation, multi-directional translation, scaling, and flipping. These operations enhance data diversity and significantly improve the model's generalization capability while preserving the integrity of pathological features. The specific training data are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Training Epochs** & **Batch Size** & **Learning Rate** & **Weight Decay** & **Optimizer** & **Loss Function** \\ \hline PH\({}^{2}\)[42] & 150 & 2 & & & & & \\ ISIC2017 [43] & 100 & 4 & \(1\times 10^{-5}\) & 0.01 & Adam & Cross-Entropy \\ ISIC2018 [44] & 100 & 4 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training data of the skin lesion dataset.  
Figure 8: Three samples from the ISIC dataset: (**a**) contains hair, (**b**) contains bubbles, and (**c**) contains shadows.