Interval Type-2 Fuzzy Programming Method for Risky Multicriteria Decision-Making with Heterogeneous Relationship

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems

Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists

Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the accumulated experience of radiologists are the two core elements related to the accuracy of identification of benign and malignant nodules. Contextual information provides comprehensive information about nodules such as location, shape, and peripheral vessels, and experienced radiologists can search for clues from previous cases as a reference to enrich the basis of decision-making. In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules. The context parsing module first segments the context structure of nodules and then aggregates contextual information for a more comprehensive understanding of the nodule. The prototype recalling module utilizes prototype-based learning to condense previously learned cases as prototypes for comparative analysis, which is updated online in a momentum way during training. Building on the two modules, our method leverages both the intrinsic characteristics of the nodules and the external knowledge accumulated from other nodules to achieve a sound diagnosis. To meet the needs of both low-dose and noncontrast screening, we collect a large-scale dataset of 12,852 and 4,029 nodules from low-dose and noncontrast CTs respectively, each with pathology- or follow-up-confirmed labels. Experiments on several datasets demonstrate that our method achieves advanced screening performance on both low-dose and noncontrast scenarios.Comment: MICCAI 202

Application of metagenomic next-generation sequencing in the diagnosis of urinary tract infection in patients undergoing cutaneous ureterostomy

ObjectiveUrinary tract infection (UTI) is an inflammatory response of the urothelium to bacterial invasion and is a common complication in patients with cutaneous ureterostomy (CU). For such patients, accurate and efficient identification of pathogens remains a challenge. The aim of this study included exploring utility of metagenomic next-generation sequencing (mNGS) in assisting microbiological diagnosis of UTI among patients undergoing CU, identifying promising cytokine or microorganism biomarkers, revealing microbiome diversity change and compare virulence factors (VFs) and antibiotic resistance genes (ARGs) after infection.MethodsWe performed a case-control study of 50 consecutive CU patients from December 2020 to January 2021. According to the clinical diagnostic criteria, samples were divided into infected group and uninfected group and difference of urine culture, cytokines, microorganism, ARGs and VFs were compared between the two groups.ResultsInflammatory responses were more serious in infected group, as evidenced by a significant increase in IFN-α (p=0.031), IL-1β (0.023) and IL-6 (p=0.018). Clinical culture shows that there is higher positive rate in infected group for most clinical pathogens like Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Candida auris etc. and the top three pathogens with positive frequencies were E. coli, K. pneumoniae, and Enterococcus faecalis. Benchmarking clinical culture, the total sensitivity is 91.4% and specificity is 76.3% for mNGS. As for mNGS, there was no significant difference in microbiome α- diversity between infected and uninfected group. Three species biomarkers including Citrobacter freundii, Klebsiella oxytoca, and Enterobacter cloacae are enriched in infected group based on Lefse. E. cloacae were significantly correlated with IL-6 and IL-10. K. oxytoca were significantly correlated with IL-1β. Besides, the unweighted gene number and weighted gene abundance of VFs or ARGs are significantly higher in infected group. Notablely, ARGs belonging to fluoroquinolones, betalatmas, fosfomycin, phenicol, phenolic compound abundance is significantly higher in infected group which may have bad effect on clinical treatment for patients.ConclusionmNGS, along with urine culture, will provide comprehensive and efficient reference for the diagnosis of UTI in patients with CU and allow us to monitor microbial changes in urine of these patients. Moreover, cytokines (IL-6, IL-1β, and IFN-a) or microorganisms like C. freundii, K. oxytoca or E. cloacae are promising biomarkers for building effective UTI diagnostic model of patients with CU and seriously the VFs and ARGs abundance increase in infected group may play bad effect on clinical treatment

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio

Spatial-temporal clustering of an outbreak of SARS-CoV-2 Delta VOC in Guangzhou, China in 2021

BackgroundIn May 2021, the SARS-CoV-2 Delta variant led to the first local outbreak in China in Guangzhou City. We explored the epidemiological characteristics and spatial-temporal clustering of this outbreak.MethodsBased on the 153 cases in the SARS-CoV-2 Delta variant outbreak, the Knox test was used to analyze the spatial-temporal clustering of the outbreak. We further explored the spatial-temporal clustering by gender and age groups, as well as compared the changes of clustering strength (S) value between the two outbreaks in Guangzhou.ResultsThe result of the Knox analysis showed that the areas at short distances and brief periods presented a relatively high risk. The strength of clustering of male-male pairs was higher. Age groups showed that clustering was concentrated in cases aged ≤ 18 years matched to 18–59 years and cases aged 60+ years. The strength of clustering of the outbreak declined after the implementation of public health measures. The change of strength of clustering at time intervals of 1–5 days decreased greater in 2021 (S = 129.19, change rate 38.87%) than that in 2020 (S = 83.81, change rate 30.02%).ConclusionsThe outbreak of SARS-CoV-2 Delta VOC in Guangzhou has obvious spatial-temporal clustering. The timely intervention measures are essential role to contain this outbreak of high transmission