A novel hybrid model combining a convolutional neural network (CNN) and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans. This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis. The model employs channel splitting to minimize parameter count. It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension. To enhance efficiency, a novel self-attention me...
