Static-Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction

Hexi Wang, Ning Xiao, Jina Zhang, Wanting Yang, Yulan Ma, Yao Suo, Juanjuan Zhao, Yan Qiang, Jianhong Lian, Qianqian Yang

Abstract: Highlights•We apply the improved Transformer approach to longitudinal tumor growth studies, using its excellent remote modeling ability to explore tumor growth trends over long time series.•We design the Local Adaptive Transformer Module, which effectively alleviates the Transformer’s insensitivity to local pixels.•We propose the Enhanced Deformable Convolution Module enriches the sampling space for feature images and learns the pixel locations of future tumor growth.•A novel cascade self-attention is adopted to establish a long-range dependence between tumor growth pixels.