提出可学习采样位置的上采样模块，提升医学图像分割细节还原能力。
DTC: A Deformable Transposed Convolution Module for Medical Image Segmentation
- 引入可变形卷积思想，动态学习上采样采样位置
- 在2D/3D数据集上显著提升特征重建与细节恢复能力
- 适合需要高精度分割的医学图像分析任务
在医学图像分割中，尤其是基于UNet的架构，上采样用于将小尺寸特征图转换为大尺寸，以实现编码器与解码器特征融合及多尺度预测。传统上采样方法如转置卷积和线性插值采用固定位置：转置卷积对预设像素或体素位置应用核元素，线性插值则基于原特征图的固定坐标分配值。这些固定位置方法可能无法捕捉预定义采样点之外的结构信息，导致伪影或细节丢失。受可变形卷积启发，本文提出一种新型上采样方法——可变形转置卷积（DTC），通过学习动态坐标（即采样位置）生成高分辨率特征图，适用于2D和3D医学图像分割任务。在3D（如BTCV15）和2D数据集（如ISIC18、BUSI）上的实验表明，DTC可有效集成至现有医学图像分割模型中，持续提升解码器的特征重构与细节恢复能力。
原文摘要 · Abstract (English)
In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones, enabling feature fusion between encoder and decoder features and supporting multi-scale prediction. Conventional upsampling methods, such as transposed convolution and linear interpolation, operate on fixed positions: transposed convolution applies kernel elements to predetermined pixel or voxel locations, while linear interpolation assigns values based on fixed coordinates in the original feature map. These fixed-position approaches may fail to capture structural information beyond predefined sampling positions and can lead to artifacts or loss of detail. Inspired by deformable convolutions, we propose a novel upsampling method, Deformable Transposed Convolution (DTC), which learns dynamic coordinates (i.e., sampling positions) to generate high-resolution feature maps for both 2D and 3D medical image segmentation tasks. Experiments on 3D (e.g., BTCV15) and 2D datasets (e.g., ISIC18, BUSI) demonstrate that DTC can be effectively integrated into existing medical image segmentation models, consistently improving the decoder's feature reconstruction and detail recovery capability.
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