QC-Net: A Hybird Quantum-Classical Neural Network Model for Medical Image Segmentation

The U-Net architecture is widely recognized as one of the most prominent models for medicalimage segmentation, comprising an encoder and a decoder. The encoder is crucial for extractingimage features to enhance segmentation accuracy, typically incorporating convolutional and poolinglayers. However, standard encoder structures often miss some classical features and struggleto extract high-level features effectively. In this paper, a hybird Quantum-Classical Neural Network(QC-Net) model with a novel encoder is proposed, aiming to capture more representativefeatures. Our encoder features a Residual Convolutional Block (RCB) to primarily extract somemissed features, and then Efficient Channel Attention (ECA) is employed into the output featuremaps after the RCB and pooling operations to handle more complex and noisy information. Consequently,a two-qubit parameterized circuit is devised to capture the final output features of theencoder, aiming to further capture the hidden high-level features from the quantum dimension.The decoder incorporates a joint attention mechanism and deconvolution operations to recoverthe spatial resolution and detail information of the original input image. To validate its efficacy,we conduct skin lesion segmentation experiments utilizing the ISIC2018 dataset. Notably, ourQC-Net model outperforms both U-Net and CA-Net, achieving an average Dice coefficient of93.2%, indicating improvements of 5.43% and 1.12%, respectively. These results underscore theoutstanding performance of our proposed QC-Net model.