"I Prefer an Everyday Style and Dislike Big Food Fighters": Integrating Foodshow into Everyday Life

In this paper, we examine a genre of User Generated Videos (UGV) centering around food which we term "foodshow". While Korean mukbang features streamers eat large quantities of food, foodshow features mundane activities in life. Toward understanding unique watching practices of foodshow given its difference from mukbang, we interviewed 12 foodshow viewers in China, uncovering active and life-oriented watching of foodshow - viewers actively choose and watch foodshow which walks a fine line between authenticity and attractiveness, reflecting everyday existence and involving moderate performance at the same time. With these unique features, foodshow watching is integrated into viewers' daily life, and helps in eating, sleeping, and emotional adjustment, in addition to entertainment. Based on the findings, we suggest design implications for healthier and more beneficial foodshow.Ope

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

The prognostic value of multiparametric cardiac magnetic resonance in patients with systemic light chain amyloidosis

BackgroundLate gadolinium enhancement (LGE) is a classic imaging modality derived from cardiac magnetic resonance (CMR), which is commonly used to describe cardiac tissue characterization. T1 mapping with extracellular volume (ECV) and native T1 are novel quantitative parameters. The prognostic value of multiparametric CMR in patients with light chain (AL) amyloidosis remains to be thoroughly investigated.MethodsA total of 89 subjects with AL amyloidosis were enrolled from April 2016 to January 2021, and all of them underwent CMR on a 3.0 T scanner. The clinical outcome and therapeutic effect were observed. Cox regression was used to investigate the effect of multiple CMR parameters on outcomes in this population.ResultsLGE extent, native T1 and ECV correlated well with cardiac biomarkers. During a median follow-up of 40 months, 21 patients died. ECV (hazard ratio [HR]: 2.087 for per 10% increase, 95% confidence interval [CI]: 1.379-3.157, P < 0.001) and native T1 (HR: 2.443 for per 100 ms increase, 95% CI: 1.381-4.321, P=0.002) were independently predictive of mortality. A novel prognostic staging system based on median native T1 (1344 ms) and ECV (40%) was similar to Mayo 2004 Stage, and the 5-year estimated overall survival rates in Stage I, II, and III were 95%, 80%, and 53%, respectively. In patients with ECV > 40%, receiving autologous stem cell transplantation had higher cardiac and renal response rates than conventional chemotherapy.ConclusionBoth native T1 and ECV independently predict mortality in patients with AL amyloidosis. Receiving autologous stem cell transplantation is effective and significantly improves the clinical outcomes in patients with ECV > 40%

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

Experimental and theoretical study on minimum achievable foil thickness during asymmetric rolling

Parts produced by microforming are becoming ever smaller. Similarly, the foils required in micro-machines are becoming ever thinner. The asymmetric rolling technique is capable of producing foils that are thinner than those produced by the conventional rolling technique. The difference between asymmetric rolling and conventional rolling is the \u27cross-shear\u27 zone. However, the influence of the cross-shear zone on the minimum achievable foil thickness during asymmetric rolling is still uncertain. In this paper, we report experiments designed to understand this critical influencing factor on the minimum achievable thickness in asymmetric rolling. Results showed that the minimum achievable thickness of rolled foils produced by asymmetric rolling with a rolling speed ratio of 1.3 can be reduced to about 30% of that possible by conventional rolling technique. Furthermore, the minimum achievable thickness during asymmetric rolling could be correlated to the cross-shear ratio, which, in turn, could be related to the rolling speed ratio. From the experimental results, a formula to calculate the minimum achievable thickness was established, considering the parameters cross-shear ratio, friction coefficient, work roll radius, etc. in asymmetric rolling

AuDi: an Auto-Feedback Display for Crowdsourcing

While feedback, by experts or peers, is found to have positive effects on crowdsourcing work, it is a costly approach as more people or time is involved in order to provide feedback. This paper explores an automatic feedback display called AuDi for crowdsourcing. AuDi shows the worker’s accuracy rate, which is automatically calculated with the use of an accuracy algorithm, by changing the background color of the task page. We conducted an experimental study with AuDi in the field, and employed both quantitative and qualitative methods for data collection and analysis. Our study shows that, without introducing new cost, such an auto-feedback display is well received by our participants, gives them assurance and more confidence, and also positively contributes to work performance by pushing them to study more and understand better the task requirements