# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 1 Introduction
Another important network architecture is the DeepLab series [14] which introduces dilated convolutions to enlarge the receptive field and combines it with conditional random fields (CRFs) for post-processing to enhance the boundary detail of the segmentation result. DeepLab is particularly suitable for processing medical images with complex backgrounds and details, such as those widely used in tumor segmentation and organ segmentation. Fully convolutional networks (FCNs) [15] are another type of architecture used for image segmentation that retains the spatial information of the image by replacing fully connected layers with convolutional layers. FCNs can be trained and predicted end-to-end, greatly simplifying the segmentation process [16]. FCN-based architectures such as SegNet [17] further improve the upsampling process and use max pooling indices to improve segmentation accuracy and computational efficiency. These features make them outstanding in real-time segmentation tasks. In recent years, Transformer architectures have gradually entered the field of medical image segmentation, leveraging their powerful ability to model long-distance dependencies [18]. TransUNet ([PERSON] et al., 2021) combines the global feature extraction ability of Transformer with the local detail restoration ability of U-Net, significantly improving the segmentation of medical images, especially in tasks that require the capture of global contextual information [19]. Multi-scale feature extraction networks such as HRNet [20] enhance the model's ability to capture both detailed and global features by maintaining feature representations at multiple resolutions simultaneously and continuously fusing information at different scales during segmentation. HRNet excels at fine boundary segmentation tasks and is suitable for precise identification of tumors and organs. In summary, these network architectures each have their own characteristics and enhance medical image segmentation through different methods. From the classic skip connections of U-Net and the dilated convolution of DeepLab to Transformer and multi-scale feature fusion, these innovations have greatly promoted the development of medical image segmentation technology. Due to the characteristics of medical image data, such as limited sample size, small targets, complex image structures, and a lack of image semantics, most medical image segmentation networks face challenges such as poor generalization and low segmentation accuracy. In recent years, with significant achievements of the self-attention mechanism in computer vision, more neural network models based on self-attention [21] have been developed. Initially, we considered using the self-attention mechanism as the basis of this research. However, extensive experiments revealed that some classic neural network models based on the self-attention mechanism did not perform well on medical datasets. Instead, replacing the backbone with ConvNext [22] significantly improved the results. Therefore, this paper explores in depth the design ideas behind ConvNext. ConvNext's most significant technical feature is its modern convolutional design, which incorporates improved feature fusion based on the self-attention mechanism [23]. Additionally, during the experimental process, nn-UNet (no-new-Net) emerged as an outstanding performer among numerous network architectures [24]. nn-UNet is an automated medical image segmentation framework based on the U-Net architecture and can automatically adapt to different medical image segmentation tasks. The design philosophy of nn-UNet aims to create a general and easy-to-use image segmentation solution through an automated configuration and optimization process, eliminating the need for extensive manual tuning and task-specific designs. This significantly enhances the model's generality and ease of use, bringing revolutionary progress to medical image analysis. Drawing from the characteristics and principles of these two technologies, we propose a novel convolutional module, the AdaptiveConv2d module.