## 5 Discussion  
DL has been recognized as a revolutionary technology that creates an explosive impact on all industries. Recent advances have explored the potential of DL-based models on cancer diagnosis [26]. As the second most common cancer among men worldwide, PCa diagnosis is not efficiently done due to a worldwide shortage of proficient pathologists. DL has become the mainstream technique behind the latest automated PCa grading systems, which significantly speed up PCa diagnosis and treatment. However, a key challenge of building an accurate grading system is to allow a DL model to learn what and where to extract the most informative features from a whole-slide image, which is usually with background noise, morphological heterogeneity of slide contents, as well as artifacts. For example, the original whole-slide images in the PANDA dataset are large and in high resolutions, while the part with colored tissue information is like a thin and long belt, only taking up a small percent of the coverage. Moreover, many methods overly rely on massive manual labeling and hand-picked RoIs, but the data available are usually incomplete and with imperfect labels. The proposed MCMS attention module is driven by the need to mining distinguishable features and patterns from a whole-slide image to detect the severity of PCa. The mission of the MCMS attention module is to empower the backbone CNN to learn the areas of concern, i.e., the channels and spatial locations that carry distinguishable information. This mission is accomplished by assigning optimized weights, namely attentions, to channels and spatial locations, so as to refine the original feature map with the learned attentions. Similar ideas have appeared in prior studies [23; 27]. The uniqueness of the proposed MCMS attention module lies in the presence of multiple MLPs with different reduction ratios and multiple convolutional layers with different kernel sizes, which offers a view with finer granularity when attending the input feature map. Due to the structural innovation, MCMS demonstrated superior performance compared to its peers. Results showed that adopting different reduction ratios and kernel sizes did help boost feature extraction, leading to performance elevation in both ACC and QWK. The proposed MCMS-assisted model can serve as a core component for building an automated PCa grading system. Beyond that, since the attention block allows the model to pinpoint areas of concern in a whole-slide image, the system can be used to train practicing pathologists. For example, an interesting use case would be developing a software tool that can generate image exercises that ask trainees to mark and grade areas of concern, which are then compared with the reference answers generated by the tool. Moreover, a software medical robot can be built to learn from a group of experienced pathologists through active and reinforcement learning, and meanwhile, to teach students with novel \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **ACC** & **QWK** \\ \hline ResNet50 & 0.6638 & 0.7766 \\ ResNet101 & 0.6761 & 0.7739 \\ \hline CBAM-Layer4 & 0.6931 & 0.7917 \\ CBAM-Layer3 and 4 & 0.7029 & 0.8132 \\ \hline MCMS-Layer4-MAX & 0.7093 & 0.7913 \\ MCMS-Layer3 and 4-MAX & 0.7102 & 0.7992 \\ MCMS-Layer4-AVG & 0.7133 & 0.8411 \\ MCMS-Layer3 and 4-AVG & **0.7179** & **0.8503** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of model evaluation in ACC and QWK. and more accurate feature patterns. Such a medical robot will make education more effective, productive, and engaging. This study is subject to some limitations that also point to future directions. First, we only tested the MCMS module on the slide images with one resolution. A potential improvement is to create a pyramid of images with different resolutions, which could allow the model to generate a pyramid of feature maps that the MCMS module can further refine.