A deep learning neural-network-based visualization prediction model for carboplatin chemoresistance in ovarian cancer patients

In this paper, we propose and construct a new model for predicting cisplatin resistance in ovarian cancer using a deep learning neural network based on multi-modal data fusion. This multimodal data consists of ovarian ultrasound images(US) and color Doppler flow images(CDF). Firstly, collect clinical multimodal data, train U-Net network, and segment regions of interest in the image. Subsequently, the trained U-Net model is used to segment the image and obtain regions of interest, namely the regions of interest in ultrasound images and blood flow images. Finally, the ultrasound images of the regions of interest are segmented and input into a diagnostic network for feature extraction. During the feature extraction process, the transformer module is used to interact and fuse the two modal data, and the features of these two modal data are comprehensively utilized to achieve visual prediction of cisplatin resistance in ovarian cancer. In the experiment, we compared this model with other network models and demonstrated that our proposed model effectively predicted drug resistance in ovarian cancer patients, maintaining approximately 81% accuracy.