Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs

As deep networks require large amounts of accurately labeled training data, a strategy to collect sufficiently large and accurate annotations is as important as innovations in recognition methods. This is especially true for building Computer Aided Detection (CAD) systems for chest X-rays where domain expertise of radiologists is required to annotate the presence and location of abnormalities on X-ray images. However, there lacks concrete evidence that provides guidance on how much resource to allocate for data annotation such that the resulting CAD system reaches desired performance. Without this knowledge, practitioners often fall back to the strategy of collecting as much detail as possible on as much data as possible which is cost inefficient. In this work, we investigate how the cost of data annotation ultimately impacts the CAD model performance on classification and segmentation of chest abnormalities in frontal-view X-ray images. We define the cost of annotation with respect to the following three dimensions: quantity, quality and granularity of labels. Throughout this study, we isolate the impact of each dimension on the resulting CAD model performance on detecting 10 chest abnormalities in X-rays. On a large scale training data with over 120K X-ray images with gold-standard annotations, we find that cost-efficient annotations provide great value when collected in large amounts and lead to competitive performance when compared to models trained with only gold-standard annotations. We also find that combining large amounts of cost efficient annotations with only small amounts of expensive labels leads to competitive CAD models at a much lower cost.Comment: MICCAI 2022, Contains Supplemental Materia

The Effect of Temperament on the Association Between Pre-treatment Anxiety and Chemotherapy-Related Symptoms in Patients With Breast Cancer

Objective Pre-treatment anxiety (PA) before chemotherapy increases complaints of chemotherapy-related symptoms (CRS). The results on the association have been inconsistent, and the effect of temperament remains unclear. We aimed to determine whether PA is a risk factor for CRS and the effect of differing temperaments on CRS. Methods This prospective study comprised 176 breast cancer patients awaiting adjuvant chemotherapy post-surgery. We assessed CRS, PA, and temperament using the MD Anderson Symptom Inventory (MDASI), the Hospital Anxiety and Depression Scale, and the short form of the Temperament and Character Inventory-Revised, respectively. The MDASI was re-administered three weeks after the first chemo-cycle. Results PA showed weak positive correlation with several CRS after the first cycle; no CRS was significantly associated with PA when pre-treatment depressive symptoms and baseline CRS were adjusted in multiple regression analysis. Moderation model analysis indicat-ed that the PA effect on several CRS, including pain, insomnia, anorexia, dry mouth, and vomiting, was moderated by harm avoidance (HA) but not by other temperament dimensions. In particular, PA was positively associated with CRS in patients with low HA. Conclusion The results in patients with low HA indicate that more attention to PA in patients with confident and optimistic temperaments is necessary

Study for regulating flow fluctuation of peristaltic pump using a spherical elastic damper

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A study on the pulsation reduction effect of a valve composed of elastic bodies.

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A study for regulating flow fluctuation of peristaltic pump using a spherical elastic damper.

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Soft damper for highly effective flow pulsation reduction

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LENS-GRM Applicability Analysis and Evaluation

Recently, there have been many abnormal natural phenomena caused by climate change. Anthropogenic factors associated with insufficient water resource management can be another cause. Among natural causes, rainfall intensity and volume often induce flooding. Therefore, accurate rainfall estimation and prediction can prevent and mitigate damage caused by these hazards. Sadly, uncertainties often hinder accurate rainfall forecasting. This study investigates the uncertainty of the Korean rainfall ensemble prediction data and runoff analysis model in order to enhance reliability and improve prediction. The objectives of this study include: (i) evaluating the spatial characteristics and applicability of limited area ensemble prediction system (LENS) data; (ii) understanding uncertainty using parameter correction and generalized likelihood uncertainty estimation (GLUE) and grid-based rainfall-runoff model (GRM); (iii) evaluating models before and after LENS-GRM correction. In this study, data from the Wicheon Basin was used. The informal likelihood (R2, NSE, PBIAS) and formal likelihood (log-normal) were used to evaluate model applicability. The results confirmed that uncertainty of the behavioral model exists using the likelihood threshold when applying the runoff model to rainfall forecasting data. Accordingly, this method is expected to enable more reliable flood prediction by reducing the uncertainties of the rainfall ensemble data and the runoff model when selecting the behavioral model for the user’s uncertainty analysis. It also provides a basis for flood prediction studies that apply rainfall and geographical characteristics for rainfall-runoff uncertainty analysis

Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (R2=0.7480–0.8318; NSE=0.7524–0.7965; MRPE=0.0807–0.0895) were obtained than those of water-level prediction results by univariate training