## 4 Results and Discussion
### Comparisons with Other Multiple Instance Methods  
To demonstrate the effectiveness of the proposed method, the algorithm was tested and compared with existing MIC methods on SRS and Mars datasets, respectively. The flowchart of different MIC methods is shown in Figure 6. To facilitate subsequent comparisons, all available patches were passed through the ImageNet-pretrained ResNet-18 model to obtain 512-d feature vectors. In the Embedded-Space paradigm MIC method, the crucial step was to fuse these instance level embeddings to obtain a bag level embedding that was suitable for applying to downstream tasks. In Tables 3 and 4, MIP (mean/max) represents utilizing common fixed MIL pooling operators. Bag level embedding was generated by operating mean or max pooling on each dimension for all instances in the same bag, as seen in Equations (14) and (15). Similarly, MIP (attention) represented utilizing an attention-based MIL pooling operator, which introduced trainable parameters **w** and **v** to allocate weights automatically for each instance by end-to-end training, as seen in Equation (16). Besides these classic MIP operators, we further compared with the RMDL network proposed by [PERSON] et al. [2], which consists of local-global feature fusion, instance recalibration and multi-instance pooling modules. After the MIL pooling layer, the bag embedding was sent into fully connected layers for final classification. Compared with fixed ones, the attention-based MIL pooling method can be more flexible. It provides information about the contribution of each instance to the final prediction, which can help to find key instances. However, due to the scarcity of patch label information, extra errors may be introduced if the classifier cannot be trained sufficiently, which was confirmed in our experimental results. On the Mars dataset with more data, the performance of the attention-based MIL pooling method was 2% worse than that of the fixed max pooling method. This suggests that with the increase in the number of instances, the classifier becomes harder to train, resulting in the final performance degradation. Compared with the attention-based MIL pooling method, the RMDL method achieved almost the same performance on the SRS dataset and a slight improvement on the Mars dataset. Essentially, the RMDL method also adaptively assigned weights to instances \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Dimension** & **Recall (\%)** & **Precision (\%)** & _F1\_Score (\%)_ \\ \hline TX\_fea & 408 & 94.78 \(\pm\) 0.013 & 94.99 \(\pm\) 0.013 & 94.59 \(\pm\) 0.012 \\ VGG-16\_fea & 4096 & 97.24 \(\pm\) 0.012 & 97.24 \(\pm\) 0.013 & 97.26 \(\pm\) 0.012 \\ ResNet-18\_fea & 512 & 98.14 \(\pm\) 0.005 & 98.14 \(\pm\) 0.005 & 98.16 \(\pm\) 0.005 \\ DenseNet-121\_fea & 1024 & 98.34 \(\pm\) 0.009 & 98.34 \(\pm\) 0.009 & 98.35 \(\pm\) 0.008 \\ EfficientNet-B0\_fea & 1280 & 97.69 \(\pm\) 0.010 & 97.69 \(\pm\) 0.010 & 97.70 \(\pm\) 0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different features in Mars dataset. Figure 6: Flowchart of the different multiple instance classification methods. by end-to-end training and fused instance features according to their scores. For these various existing methods, topological structural information is not considered to integrate into the final bag-level embedding in the process of instance feature fusion.