In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

It has become a trend in recent years to use deep neural networks for colorization. However, previous methods often encounter problems with edge color leakage and difficulties in obtaining a plausible color output from the Euclidean distance. To solve these problems, we propose a new adversarial edgeaware image colorization method with multitask output combined with semantic segmentation. The system uses a generator with a deep semantic fusion structure to infer semantic clues in a given grayscale image under chroma conditions and learns colorization by simultaneously predicting color information and semantic information. In addition, we also use a specific color difference loss with characteristics of human visual observation that is combined with semantic segmentation loss and adversarial loss for training. The experimental results show that our method is superior to existing methods in terms of different quality metrics and achieves good results in image colorization.

In the field of computer vision, super-resolution reconstruction techniques based on deep learning have undergone considerable advancement; however, certain limitations remain, such as insufficient feature extraction and blurred image generation. To address these problems, we propose an image super-resolution reconstruction model based on a generative adversarial network. First, we employ a dual network structure in the generator network to solve the problem of insufficient feature extraction. The dual network structure is divided into an upsample subnetwork and a refinement subnetwork, which upsample and optimize a low-resolution image, respectively. In a scene with large upscaling factors, this structure can reduce the negative effect of noise and enhance the utilization of high-frequency details, thereby generating high-quality reconstruction results. Second, to generate sharper super-resolution images, we use the perceptual loss, which exhibits a fast convergence and excellent visual effect, to guide the generator network training. We apply the ResNeXt-50-32x4d network, which has few parameters and a large depth, to calculate the loss to obtain a reconstructed super-resolution image that is highly realistic. Finally, we introduce the Wasserstein distance into the discriminator network to enhance the discrimination ability and stability of the model. Specifically, this distance is employed to eliminate the activation function in the last layer of the network and avoid the use of the logarithm in calculating the loss function. Extensive experiments on the DIV2K, Set5, Set14, and BSD100 datasets demonstrate the effectiveness of the proposed model.

No abstract

No abstract