Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering

Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets based on the knowledge of known classes. Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories. We then propose an uncertainty-aware multi-view cross pseudo-supervision strategy, which is trained jointly on all categories of data using pseudo labels generated by a self-labeling strategy. Finally, we further refine the pseudo label by aggregating neighborhood information through local sample similarity to improve the clustering performance of the model for unknown categories. We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories. We also further validated the effectiveness of the different modules through extensive ablation experiments. Our code will be released soon.Comment: 10 pages, 1 figure,Accepted by miccai 202

Enhancing Hydrogen Storage in AZ31 Alloy through Pd/G Composite

In this research, we investigated the catalytic effects of Palladium/Graphene(Pd/G) on AZ31 alloy for hydrogen storage. X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (SEM-EDS) were employed to confirm the homogeneous distribution of AZ31 and observe phase changes after mechanical alloying with the catalysts. The hydrogen storage properties of AZ31 with catalysts were systematically examined, and the time of maximum reaction rate for nucleation was determined using Avarami Plot. The results of the study show that the incorporation of 2% Pd/G resulted in the fastest hydrogen absorption and desorption time, taking 200 seconds to achieve 90% hydrogen storage with a maximum of 6.04 wt%. The corresponding maximum hydrogen desorption occurred in 694 seconds, reaching 6.03 wt%. Consequently, the introduction of 2% Pd/G catalyst proved to be effective in significantly enhancing the hydrogen ab/desorption rates of AZ31 alloy

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

Herb-Drug Pharmacokinetic Interaction of a Traditional Chinese Medicine Jia-Wei-Xiao-Yao-San with 5-Fluorouracil in the Blood and Brain of Rat Using Microdialysis

According to a survey from the National Health Insurance Research Database (NHIRD), Jia-Wei-Xiao-Yao-San (JWXYS) is the most popular Chinese medicine for cancer patients in Taiwan. 5-Fluorouracil (5-FU) is a general anticancer drug for the chemotherapy. To investigate the herb-drug interaction of JWXYS on pharmacokinetics of 5-FU, a microdialysis technique coupled with a high-performance liquid chromatography system was used to monitor 5-FU in rat blood and brain. Rats were divided into four parallel groups, one of which was treated with 5-FU (100 mg/kg, i.v.) alone and the remaining three groups were pretreated with a different dose of JWXYS (600, 1200, or 2400 mg/kg/day for 5 consecutive days) followed by a combination with 5-FU. This study demonstrates that 5-FU with JWXYS (600 mg/kg/day or 1200 mg/kg/day) has no significant effect on the pharmacokinetics of 5-FU in the blood and brain. However, JWXYS (2400 mg/kg/day) coadministered with 5-FU extends the elimination half-life and increases the volume of distribution of 5-FU in the blood. The elimination half-life of 5-FU in the brain for the pretreatment group with 2400 mg/kg/day of JWXYS is significantly longer than that for the group treated with 5-FU alone and also reduces the clearance. This study provides practical dosage information for clinical practice and proves the safety of 5-FU coadministered with JWXYS

Ocular safety of sildenafil citrate when administered chronically for pulmonary arterial hypertension: results from phase III, randomised, double masked, placebo controlled trial and open label extension

Objective To assess the ocular effects and safety profile of chronic sildenafil oral dosing in patients with pulmonary arterial hypertension

EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e. dark corners, dense hairs), leading to poor generalization in unseen environments. To address this issue, we propose a novel domain generalization method called EPVT, which involves embedding prompts into the vision transformer to collaboratively learn knowledge from diverse domains. Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset. To facilitate knowledge sharing and the interaction of different prompts, we introduce a domain prompt generator that enables low-rank multiplicative updates between domain prompts and the shared prompt. A domain mixup strategy is additionally devised to reduce the co-occurring artifacts in each domain, which allows for more flexible decision margins and mitigates the issue of incorrectly assigned domain labels. Experiments on four out-of-distribution datasets and six different biased ISIC datasets demonstrate the superior generalization ability of EPVT in skin lesion recognition across various environments. Our code and dataset will be released at https://github.com/SiyuanYan1/EPVT.Comment: 12 pages, 5 figure

The use of one-stage meta-analytic method based on individual participant data for binary adverse events under the rule of three: a simulation study

Objective In evidence synthesis practice, dealing with binary rare adverse events (AEs) is a challenging problem. The pooled estimates for rare AEs through traditional inverse variance (IV), Mantel-Haenszel (MH), and Yusuf-Peto (Peto) methods are suboptimal, as the biases tend to be large. We proposed the “one-stage” approach based on multilevel variance component logistic regression (MVCL) to handle this problem. Methods We used simulations to generate trials of individual participant data (IPD) with a series of predefined parameters. We compared the performance of the MVCL “one-stage” approach and the five classical methods (fixed/random effect IV, fixed/random effect MH, and Peto) for rare binary AEs under different scenarios, which included different sample size setting rules, effect sizes, between-study heterogeneity, and numbers of studies in each meta-analysis. The percentage bias, mean square error (MSE), coverage probability, and average width of the 95% confidence intervals were used as performance indicators. Results We set 52 scenarios and each scenario was simulated 1,000 times. Under the rule of three (a sample size setting rule to ensure a 95% chance of detecting at least one AE case), the MVCL “one-stage” IPD method had the lowest percentage bias in most of the situations and the bias remained at a very low level (<10%), when compared to IV, MH, and Peto methods. In addition, the MVCL “one-stage” IPD method generally had the lowest MSE and the narrowest average width of 95% confidence intervals. However, it did not show better coverage probability over the other five methods. Conclusions The MVCL “one-stage” IPD meta-analysis is a useful method to handle binary rare events and superior compared to traditional methods under the rule of three. Further meta-analyses may take account of the “one-stage” IPD method for pooling rare event data

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure

Risk factors for major adverse cardiovascular events in phase III and long‐term extension studies of tofacitinib in patients with rheumatoid arthritis

Objective: Tofacitinib is an oral JAK inhibitor for the treatment of rheumatoid arthritis (RA). This study was undertaken to evaluate the risk of major adverse cardiovascular events (MACE) in patients with RA receiving tofacitinib. Methods: Data were pooled from patients with moderately to severely active RA receiving ≥1 tofacitinib dose in 6 phase III and 2 long‐term extension studies over 7 years. MACE (myocardial infarction, stroke, cardiovascular death) were independently adjudicated. Cox regression models were used to evaluate associations between baseline variables and time to first MACE. Following 24 weeks of tofacitinib, changes in variables and time to future MACE were evaluated after adjusment for age, baseline values, and time‐varying tofacitinib dose. Hazard ratios and 95% confidence intervals were calculated. Results: Fifty‐two MACE occurred in 4,076 patients over 12,873 patient‐years of exposure (incidence rate 0.4 patients with events per 100 patient‐years). In univariable analyses of baseline variables, traditional cardiovascular risk factors and glucocorticoid and statin use were associated with MACE risk; disease activity and inflammation measures were not. In subsequent multivariable analyses, baseline age, hypertension, and the total cholesterol to high‐density lipoprotein (HDL) cholesterol ratio remained significantly associated with risk of MACE. After 24 weeks of treatment, an increase in HDL cholesterol and a decrease in the total to HDL cholesterol were associated with decreased MACE risk; changes in total cholesterol, low‐density lipoprotein (LDL) cholesterol, and disease activity measures were not. Increased erythrocyte sedimentation rates trended with increased future MACE risk. Conclusion: In this post hoc analysis, after 24 weeks of tofacitinib treatment, increased HDL cholesterol, but not increased LDL cholesterol or total cholesterol, appeared to be associated with lower future MACE risk. Further data are needed to test the cardiovascular safety of tofacitinib