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\journal{Medical Image Analysis}
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	\begin{frontmatter}
		\title{Multi-Site MRI Harmonization via Attention-Guided Deep Domain Adaptation for Brain Disorder Identification -- {\em Supplementary Materials} }

		
\author[mymainaddress]{Hao Guan}%\ead{guanhao@email.unc.edu}
\author[mymainaddress,mysecondaryaddress]{Yunbi~Liu}%\ead{yunbi@email.unc.edu}
\author[mymainaddress]{Erkun Yang}%\ead{ekyang@email.unc.edu}
\author[mymainaddress]{Pew-Thian Yap}%\ead{ptyap@med.unc.edu}
\author[mymainaddress]{Dinggang~Shen}%\ead{idea.uncch@gmail.com}
\author[mymainaddress]{Mingxia~Liu\corref{mycorrespondingauthor}}%\ead{mxliu@med.unc.edu}
%\cortext[mycorrespondingauthor]{Corresponding author}\ead{dgshen@med.unc.edu}

\cortext[mycorrespondingauthor]{Corresponding author: Mingxia Liu (Email: mxliu@med.unc.edu)}

\address[mymainaddress]{Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA}
\address[mysecondaryaddress]{School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China}

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{\color{blue}
In the following, we study the influence of fine-tuning strategy using labeled target data for training the proposed attention-guided deep domain adaptation (AD$^2$A) model. 
We also investigate the influence of different ratios of target samples on the performance of our method, with details given below.

%% %% %% ---------------------------------------------------%% %% ^_^
\section{Fine-tuning with Labeled Target Data}
In the main text, no label information is available for target data. 
In this group of experiments, we'd like to investigate whether using a part of labeled target data can boost the learning performance. 
Specifically, we first utilize a part (i.e., 20\%) of randomly selected labeled target samples to fine-tune the pre-trained network and then test its performance on the target domain. 
We perform AD vs. CN classification, with ADNI-1 and ADNI-2 as the source domain and the target domain (i.e., ADNI-1$\rightarrow$ ADNI-2), respectively. 
%We select the AD vs. CN task with the setting (ADNI-1$\rightarrow$ ADNI-2). 
To avoid bias in the random selection process, we conduct the experiment five times and report the results in terms of AUC values in Fig.~\ref{fig: fine-tuning}.
From Fig.~\ref{fig: fine-tuning}, we can see the AUC values are around 0.95 which is better than the result (i.e., 0.94 as shown in Table 2 in the main text) without any labeled training data. %quite a good classification performance. 
This implies that using a small proportion of target data for fine-tuning can achieve good transferability on the target data. 
%In addition, the AUC values only have a variation of 0.85, which shows the stability and robustness of our designed network on the classification task.

\begin{figure}[!tbp]
\center
 \includegraphics[width= 0.5\textwidth]{fine-tuning.pdf}
 %\vspace{0pt}
 \caption{{\color{blue}Classification results (i.e., AUC) of the proposed AD$^2$A model in AD vs. CN classification with the fine-tuning strategy (i.e., using 20\% randomly selected labeled target data for model fine-tuning). To avoid the bias introduced by random selection, the experiment was performed five times.}}
 \label{fig: fine-tuning}
\end{figure}


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\section{Influence of Target Data}
We study the influence of the number of target samples (without any ground-truth label) on the harmonization performance. 
To this end, we utilize different ratios of target data to train the proposed AD$^2$A model, where no label information of target data is available during training. 
We focus on the task of AD vs. CN classification by treating the ADNI-1 as the source domain and ADNI-2 as the target domain (i.e., ADNI-1$\rightarrow$ ADNI-2). 
The classification results in terms of accuracy are shown in Fig.~\ref{fig: target_number}.
From Fig.~\ref{fig: target_number}, it can be seen that the classification accuracy increases when using more target data for model training. 
This implies that more target data enables that network to achieve better transferability on the target domain.

\begin{figure}[!tbp]
\center
 \includegraphics[width= 0.5\textwidth]{target_number.pdf}
 %\vspace{0pt}
 \caption{{\color{blue}Classification accuracy using different ratios of target data for training the proposed AD$^2$A model in the task of AD vs. CN classification, with ADNI-1 and ADNI-2 as the source and the target domains, respectively.}}
 \label{fig: target_number}
\end{figure}
}


%\section*{References}
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