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.
Person Re-identification is a sub-problem of image retrieval, using computer vision techniques to judge whether a certain identical pedestrian exists among different images or video sequences, which has attracted more and more attention of researchers. In this paper, regarding the fact that under non-overlapping multi-camera, traditional handcrafted features have a limited presentation power in re-identifying the pedestrians and that deep features have complicated parameters while training. A re-identification method based on the deep fusion of handcrafted features and deep features was proposed, which cut down the number of parameters but still guaranteed the accuracy, achieving the advancement of both precision and capacity. In our model, the LOMO algorithm is used to extract the handcrafted features from the images first. Then, the dimensionality of those features are reduced by Guassian Pooling for efficiency. After that, they are connected to the deep fusion network with the deep features extracted from the same images by a modification of ResNet50. Finally, the fused features are sent to the classifier for the re-identification. In the training process, we proposed a training strategy called Gradient Freezing after studying the training details in the application of transfer learning on neural network. Experiments have proved that the accuracy of applying the deep fusion network that fused with deep features and handcrafted features is 30% higher than that of the ResNet50 alone, and that the time it consumes is reduced by 10 epoches through the gradient freezing method. Moreover, several experiments carried out on dataset Marketl501 indicate that under Single Query on Marketl501, Rankl(the probability of matching successfully for the first time) can reach a high number of 81.74% and mAP(mean average Precision) of 68.75%.
In recent years, large-scale person re-identification has attracted a lot of attention from video surveillance. Usual approaches addressing this task either learn the effective feature embeddings or design the learning architectures to obtain discriminative metrics. Most of them only focus on improving the accuracy of recognition but neglect retrieval efficiency. To improve the accuracy and efficiency of person re-identification simultaneously, an accurate and fast method is proposed based on the bag of visual words (BoVW) model, which has widely been applied in image retrieval. A bag of local features is developed to simplify feature representation for person re-identification. Cross-view dictionary learning is used to eliminate the redundancy of these local features. These local features consist of scale invariant feature transform and local maximal occurrence representation (LOMO) that are invariant in scale and color, respectively. Finally, integrated BoVW histograms are obtained, which encode the images by k-means clustering. Experiments conducted on the CUHK03, Market1501, and MARS datasets show that the proposed method performs favorably against existing approaches.
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