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In this research, 106 patients with DTC were selected as the thyroid cancer group, and 106 patients with thyroid adenoma in the same period were selected as the thyroid adenoma group. The ultrasound elastography parameters (elasticity ratio, blue area ratio), serum midkine (MK) and vascular endothelial growth factor (VEGF) levels of patients in two groups were tested and compared. The study found that the elasticity ratio, blue area ratio, serum MK, and VEGF levels were related to lymph node metastasis, capsule invasion and clinical staging in DTC patients (P<0.05). The area under the curve (AUC) for the combined diagnosis of DTC by elasticity ratio, blue area ratio, serum MK and VEGF levels is 0. 888. In tissues of DTC patients, elasticity ratio, blue area ratio, serum MK and VEGF levels were positively correlated with the expression of chemokine receptor (CXCR) 4, disaggregated metalloproteinase (ADAM) 9 and targeting Xklp2 target protein (TPX2) gene, while negatively correlated with the expression of programmed cell death factor (PDCD) 4 gene. It was suggested that the up-regulation of ultrasound elastography parameters, serum MK and VEGF levels may evaluate the condition of patients with DTC and the degree of tumor malignancy. The combination of the three has a reliable diagnostic value for DTC and can provide new ideas for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this article, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multiencoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets verify that our method outperforms existing techniques in terms of both enhancement quality and downstream task performance. The code and the newly collected datasets are publicly available at https://github.com/ChunmingHe/HQG-Net.

Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the L 2,1 -norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods.