Learning to Purify Noisy Labels via Meta Soft Label Corrector

Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by designing a method to identity suspected noisy labels and then correct them. Current approaches to correcting corrupted labels usually need certain pre-defined label correction rules or manually preset hyper-parameters. These fixed settings make it hard to apply in practice since the accurate label correction usually related with the concrete problem, training data and the temporal information hidden in dynamic iterations of training process. To address this issue, we propose a meta-learning model which could estimate soft labels through meta-gradient descent step under the guidance of noise-free meta data. By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, we could adaptively obtain rectified soft labels iteratively according to current training problems without manually preset hyper-parameters. Besides, our method is model-agnostic and we can combine it with any other existing model with ease. Comprehensive experiments substantiate the superiority of our method in both synthetic and real-world problems with noisy labels compared with current SOTA label correction strategies.Comment: 12 pages,6 figure

Effect of Baicalin on inflammatory mediator levels and microcirculation disturbance in rats with severe acute pancreatitis

Objective: To investigate the effect of Bacailin on inflammatory mediator levels and microcirculation disturbance in severe acute pancreatitis (SAP) rats and explore its therapeutic mechanism on this disease. Methods: SAP model rats were randomly divided into model control group and Baicalin treated group, 45 rats in each group. The same number of normal rats were included in sham-operated group. These groups were further subdivided into 3 h, 6 h and 12 h subgroups, respectively (15 rats in each subgroup). At 3, 6 and 12 hours after operation, rats were killed to conduct the following experiments: (1) to examine the mortality rates of rats, the ascites volume and pancreatic pathological changes in each group; (2) to determine the contents of amylase, PLA~2~, TXB~2~, PGE~2~, PAF and IL-1[beta]; in blood as well as the changes in blood viscosity.Results: (1) Compared to model control group, treatment with Baicalin is able to improve the pathological damage of the pancreas, lower the contents of amylase and multiple inflammatory mediators in blood, decrease the amount of ascitic fluid and reduce the mortality rates of SAP rats; (2) at 3 hours after operation, the low-shear whole blood viscosity in Baicalin treated group was significantly lower than that in model control group;at 12 hours after operation, both the high-shear and low-shear whole blood viscosity in Baicalin treated group were also significantly lower than those in model control group.Conclusion: Baicalin, as a new drug, has good prospects in the treatment of SAP since it can exert therapeutic effects on this disease through inhibiting the production of inflammatory mediators, lowering blood viscosity, improving microcirculation and mitigating the pathological damage of the pancreas

Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation

In this paper, we consider the network latency estimation, which has been an important metric for network performance. However, a large scale of network latency estimation requires a lot of computing time. Therefore, we propose a new method that is much faster and maintains high accuracy. The data structure of network nodes can form a matrix, and the tensor model can be formed by introducing the time dimension. Thus, the entire problem can be be summarized as a tensor completion problem. The main idea of our method is improving the tensor leverage sampling strategy and introduce tensor QR decomposition into tensor completion. To achieve faster tensor leverage sampling, we replace tensor singular decomposition (t-SVD) with tensor CSVD-QR to appoximate t-SVD. To achieve faster completion for incomplete tensor, we use the tensor $L_{2,1}$-norm rather than traditional tensor nuclear norm. Furthermore, we introduce tensor QR decomposition into alternating direction method of multipliers (ADMM) framework. Numerical experiments witness that our method is faster than state-of-art algorithms with satisfactory accuracy.Comment: 20 pages, 7 figure

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates

Structured sparse model based feature selection and classification for hyperspectral imagery

Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large num-ber of feature variables exist but the amount of training sam-ples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A lin-ear sparse logistic regression model is proposed to combine feature selection and pixel classification into a regularized op-timization problem with the constraint of sparsity. To explore the structured features, three-dimensional discrete wavelet transform (3D-DWT) is employed, which processes the hy-perspectral data cube as a whole tensor instead of adapting the data to a vector or matrix. This allows more effective capturing of the spatial and spectral structure. The structure of the 3D-DWT features is imposed on the sparse model by group LASSO which selects the features on the group level. The advantages of our method are validated on the real hyperspectral data

Ion channels in cancer-induced bone pain: from molecular mechanisms to clinical applications

Cancer-induced bone pain (CIBP) caused by bone metastasis is one of the most prevalent diseases, and current treatments rely primarily on opioids, which have significant side effects. However, recent developments in pharmaceutical science have identified several new mechanisms for CIBP, including the targeted modification of certain ion channels and receptors. Ion channels are transmembrane proteins, which are situated on biological cell membranes, which facilitate passive transport of inorganic ions across membranes. They are involved in various physiological processes, including transmission of pain signals in the nervous system. In recent years, there has been an increasing interest in the role of ion channels in chronic pain, including CIBP. Therefore, in this review, we summarize the current literature on ion channels, related receptors, and drugs and explore the mechanism of CIBP. Targeting ion channels and regulating their activity might be key to treating pain associated with bone cancer and offer new treatment avenues