面向医学影像报告生成的门归一化编解码网络OA北大核心CSTPCD

Automatic generation of medical image reports can alleviate the workload of doctors and reduce the rate of misdiagnosis or missed diagnosis.Because of the uniqueness of medical images,lesions are usually small,and the gray difference between them and normal areas is hard to differentiate,resulting in loss of keywords in text generation and in-accurate reporting.Herein,a gated normalized encoder-decoder network for medical image report generation is de-veloped,which optimizes visual feature extraction through the gated channel transformation unit,enhances the differ-ence between features,and automatically screens key features.A gate normalization algorithm is designed to combine contextual information along with the channel dimensions,activate the neurons between channels in the shallow net-work,inhibit the neuron activity in the deep network,and filter invalid features,allowing full interaction between text and visual semantics to enhance the quality of report generation.Experimental results on two widely used reference datasets,IU X-Ray and MIMIC-CXR,reveal that the model can achieve advanced performance and generate image re-ports with better visual semantic consistency.