The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of and on JSRT datasets and and on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding.

Deep learning techniques have provided new research methods for computer-aided diagnosis, allowing researchers to use deep learning methods to process medical imaging data. Chest X-ray examinations are widely used as a primary screening method for chest diseases. Therefore, it is of great importance to study diagnosis of 14 common pathologies in chest X-ray images using deep learning methods. In this paper, we propose a deep learning model named AM_DenseNet for chest X-ray image classification. The model adopts a dense connection network and adds an attention module after each dense block to optimize the model’s ability to extract features, and finally a Focal Loss function is applied to solve the data imbalance problem. The experiments used chest X-ray images as model input and were trained to output the probabilities of 14 chest pathologies. The Area under the ROC curve (AUC) was used to measure the classification results, and the final average AUC was 0.8537. The experimental results show that the AM_DenseNet model could complete the pathology classification of the chest X-ray images effectively.

Background: Fundus fluorescein angiography (FFA) is an imaging method used to assess retinal vascular structures by injecting exogenous dye. FFA images provide complementary information to that provided by the widely used color fundus (CF) images. However, the injected dye can cause some adverse side effects, and the method is not suitable for all patients. Methods: To meet the demand for high-quality FFA images in the diagnosis of retinopathy without side effects to patients, this study proposed an unsupervised image synthesis framework based on dual contrastive learning that can synthesize FFA images from unpaired CF images by inferring the effective mappings and avoid the shortcoming of generating blurred pathological features caused by cycle-consistency in conventional approaches. By adding class activation mapping (CAM) to the adaptive layer-instance normalization (AdaLIN) function, the generated images are made more realistic. Additionally, the use of CAM improves the discriminative ability of the model. Further, the Coordinate Attention Block was used for better feature extraction, and it was compared with other attention mechanisms to demonstrate its effectiveness. The synthesized images were quantified by the Fréchet inception distance (FID), kernel inception distance (KID), and learned perceptual image patch similarity (LPIPS). Results: The extensive experimental results showed the proposed approach achieved the best results with the lowest overall average FID of 50.490, the lowest overall average KID of 0.01529, and the lowest overall average LPIPS of 0.245 among all the approaches. Conclusions: When compared with several popular image synthesis approaches, our approach not only produced higher-quality FFA images with clearer vascular structures and pathological features, but also achieved the best FID, KID, and LPIPS scores in the quantitative evaluation.

In clinical applications, the task of medical image quality assessment is done by radiologists and the existence of subjective factors leads to unstable the accuracy of the assessment. Deep learning methods bring the opportunity for objective assessment image quality. In order to avoid the overfitting problem of deep learning methods in small-sample data assessment, this paper proposes a medical image quality assessment method based on residual learning. This method adds a residual module to the traditional quality assessment network, so that the problem of overfitting can be effectively alleviated in the process of deepening the network structure. At the same time, this paper initially establishes a chest X-ray image quality assessment dataset. In the experiment, the correlation coefficients SRCC and PLCC of this method reaches 0.9692 and 0.9589, respectively, and the mean square error is 0.6697. Compared with the six assessment methods, three indicators are optimal. The method proposed in this paper can meet the clinical assessment needs of medical image quality, and the effectiveness of the method has been fully verified.