## 4 Results and Analysis  
In this section, we report the experimental results by comparing the performance of different models on the PANDA dataset. The models that were evaluated are described as follows:  
* ResNet50 and ResNet101 are two popular ResNet [24] variants, with 50 layers and 101 layers deep, respectively.
* To keep a consistent CNN architecture, we chose ResNet50 as the backbone network, and integrated the CBAM or MCMS into the backbone to create various models. For CBAM, two models, including CBAM-Layer4 and CBAM-Layer3 and 4, were evaluated; the CBAM-Layer4 had the CBAM module integrated into the fourth convolutional layer, and CBAM-Layer3 and 4 had the CBAM module integrated into the third and fourth layers.
* For MCMS, four models, including MCMS-Layer4-MAX, MCMS-Layer3 and 4-MAX, MCMS-Layer4-AVG, and MCMS-Layer3 and 4-AVG, were evaluated, in which the MAX/AVG suffix in the model names specify the pooling operation applied to the outputs of the parallel convolutional layers within the multi-spatial attention layer.  
Our empirical results showed that adding the attention module to the early layers of the backbone network was more effective, indicating that the low-level features play a more crucial role in building a strong model. Table 2 shows the performance of tested models in ACC and QWK. The observations and insights are highlighted as follows:  
* ResNet101 outperformed ResNet50 by 1.2% in ACC, and was on a par with ResNet50 in QWK, meaning that simply adding more layers to the ResNet has limited effect.
* The improvement led by the attention module was significant. With one CBAM block added, CBAM-Layer4 outperformed ResNet101 by 1.7% in ACC and by 1.78% in QWK. Our best model, MCMS-Layer3 and 4-AVG outperformed ResNet101 by 4.18% in ACC and by 7.64% in QWK.
* Adding the attention module to multiple layers of the backbone network brought marginal improvement, and this was the case for both CBAM and MCMS, with an average improvement of 0.52% and 1.29%, in ACC and QWK, respectively. The results were from a comparison of CBAM-Layer3 and 4 vs. CBAM-Layer4, MCMS-Layer3 and 4-MAX vs. MCMS-Layer4-MAX, and MCMS-Layer3 and 4-AVG vs. MCMS-Layer4-AVG.
* We observed that an averaging pooling was more effective than a max pooling when aggregating the outputs of the convolutional layers within the spatial attention layer, with an average improvement of 0.59% and 5.02%, in ACC and QWK, respectively, after a comparison of MCMS-Layer4-MAX vs. MCMS-Layer4-AVG and MCMS-Layer3 and 4-MAX vs. MCMS-Layer3 and 4-AVG. Intuitively, a max pooling is better at extracting sharp, dark, and extreme features, while an average pooling encourages the network to identify all discriminative regions of an object [25]. Our task was benefit from pooling over activations across several pooling neighborhoods, and an average pooling gave a strong signal in middle and soft edges, leaving us with more information on where the edges of the feature were localized, which was lost with max pooling.  
A fair comparison between CBAM and MCMS is CBAM-Layer3 and 4 vs. MCMS-Layer3 and 4-AVG, and results showed that the latter outperformed the former by 1.5% in ACC and by 3.71% in QWK, demonstrating the efficacy of adopting multiple reduction ratios and convolutional kernel sizes.