## 4 Experiments
### Results on COVID-HSS Dataset
#### 4.3.2 Quantitative Results on COVID-HSS  
As shown in Tables 2-4 along with the quantitative comparison of different dataset results, respectively, our proposed transYLmtMT achieves the best performance across key metrics compared to other methods, as indicated by the red labels. Among them, the red indicator is the best of all methods, and the blue indicator is the second best of the \(S_{\alpha}\), \(E_{\phi}^{mean}\) and \(MAE\) indicators. The transYLmtMT indicators in this paper are better than the baseline method in this paper. This demonstrates the benefits of our implicit reverse attention and explicit edge attention modules in providing robust feature representations. By modeling region and boundary cues in a mutually reinforcing multi-task framework, we enable precise localization of infection sites.  
Moreover, our transYLmtMT is a general infection segmentation approach that is easily adapted to other infections. By learning fundamental visual patterns of pathological morphology, it can transfer to related viruses or diseases that demonstrate similar imaging traits. Table 2 highlights transYLmtMT's state-of-the-art accuracies while underscoring its interpretability and wider applicability for enhanced clinical utility across visualization challenges. The flexible self-attention approach advances the capability for AI systems to reliably aid frontline healthcare.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** & **Dice** & **Sen.** & **Spec.** & \(S_{\alpha}\) & \(E_{\phi}^{mean}\) & **MAE** \\ \hline \multirow{6}{*}{COVID-HSSerious} & U-Net [25] & 0.279 & 0.338 & 0.754 & 0.607 & 0.665 & 0.019 \\ \cline{2-8}  & AttU-Net [26] & 0.254 & 0.308 & 0.738 & 0.586 & 0.670 & 0.018 \\ \cline{2-8}  & Inf-Net [50] & 0.349 & 0.590 & 0.804 & 0.586 & 0.652 & 0.045 \\ \cline{2-8}  & Trans-Inf-Net [19] & 0.570 & 0.737 & 0.973 & 0.678 & 0.699 & 0.026 \\ \cline{2-8}  & transYLmtMT & **0.821** & **0.857** & **0.979** & **0.930** & **0.910** & **0.012** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results on COVID-HSS ordinary dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** & **Dice** & **Sen.** & **Spec.** & \(S_{\alpha}\) & \(E_{\phi}^{mean}\) & **MAE** \\ \hline \multirow{6}{*}{COVID-HSSerious} & U-Net [25] & 0.287 & 0.349 & 0.784 & 0.612 & 0.668 & 0.019 \\ \cline{2-8}  & AttU-Net [26] & 0.266 & 0.321 & 0.755 & 0.593 & 0.668 & 0.019 \\ \cline{2-8}  & Inf-Net [50] & 0.357 & 0.592 & 0.816 & 0.590 & 0.654 & 0.046 \\ \cline{2-8}  & Trans-Inf-Net [19] & 0.578 & 0.742 & 0.973 & 0.686 & 0.706 & 0.026 \\ \cline{2-8}  & transYLmtMT & **0.824** & **0.859** & **0.979** & **0.936** & **0.912** & **0.013** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result on COVID-HSS serious dataset.  
Our transYLmtMT uses a coarse-to-fine strategy, first roughly localizing infected areas using the transYL network, then refining the contours using multiple edge attention modules. This mirrors how clinicians work--inspecting broadly first before carefully scrutinizing the boundaries. This demonstrates the benefits of the reverse attention and edge attention modules in providing robust feature representations. By concurrently modeling infection regions and boundaries in a mutually reinforcing multi-task framework, precise localization is enabled.  
In summary, harnessing localization cues and semi-supervised learning allows richer features to be captured, more effective handling of ambiguities, and the delineation of abnormalities with improved precision. This translates to considerable qualitative and quantitative improvements in segmentation performance.