# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Analysis of Experimental Results  
Through the experimental results of this study, we can clearly see that AdaptiveConv2d effectively improves the performance of medical image segmentation models on multiple datasets through its unique design. Based on the combination of innovative designs such as global context information, the residual connection mechanism, and energy loss regularization, AdaptiveConv2d shows significant advantages in different segmentation tasks. The following is a further analysis based on the experimental results and model design. First, the introduction of global context information plays a key role in medical image segmentation. By adaptively adjusting the channel weights, AdaptiveConv2d is able to effectively integrate global information after each layer of convolution, thus enhancing the ability to capture complex features. For example, in the HAM10000 dataset, AdaptiveConv2d improves the Dice coefficient by 1.21% and the IoU by 2.07%, and the key behind these improvements is the introduction of global contextual awareness, which enables the model to better capture details when dealing with skin lesions, especially in the irregular lesion areas, which are often not efficiently extracted by traditional convolutional networks. Global context information not only helps the network to enhance the representation of local features, but also improves the robustness of the model in complex contexts. Similarly, in the Blood Cell dataset, despite the high base performance, AdaptiveConv2d still improves in accuracy. the Dice coefficient improves by 0.47% and the IoU improves by 0.82%. This result shows that even on high-quality image datasets, AdaptiveConv2d is still able to refine the segmentation effect and reduce the error, demonstrating its stability and wide applicability. This consistent, small improvement also reflects AdaptiveConv2d's strengths in detail extraction and background noise processing. It is worth noting that datasets such as this one, which already have high accuracy, usually have segmentation results that stay in a bottleneck due to the complexity and nuances of feature information, and AdaptiveConv2d's ability to further optimize on such a basis demonstrates that it has a strong potential to further improve its performance. Experimental results on the COVID-19 dataset further validate the enhancement of global context information on complex structures. When dealing with irregular and contextually complex lesion regions, AdaptiveConv2d enhances the accuracy and robustness of the model, with a 0.71% improvement in the Dice coefficient and a 1.10% improvement in the IoU. Especially when dealing with lung infection regions, traditional convolutional methods tend to handle complex boundaries poorly, whereas AdaptiveConv2d optimizes the generation of channel weights by introducing an energy loss and regularization strategy, which ensures the global consistency of the features and enables the model to define the lesion regions more accurately during segmentation. The introduction of the residual linkage mechanism is another design that makes AdaptiveConv2d an outstanding performer. In the Kvasir dataset, the model improves the Dice coefficient by 1.57% and the IoU by 2.66% when confronted with complex background and illumination variations. Residual connectivity avoids the problems of information loss and gradient vanishing by preserving the original input features and ensuring that the information is propagated efficiently at each layer, especially in deep networks where residual connectivity can significantly improve the efficiency of gradient flow. Particularly in dealing with complex structures, such as target regions in endoscopic images, residual connectivity enhances the model's sensitivity to complex shapes and fine structures, reduces interference caused by background noise, and improves segmentation accuracy.