图形文件中框架的获取

分析比较了框架和ＡｕｔｏＣＡＤ图形结构的共性，提出了一从ＡｕｔｏＣＡＤ图形中获取图形信息的框架的方法，给出了两个实现框架获取的函数和命令以及一个简单的实例。     

一种基于粗糙集理论的心血管功能辅助诊断方法

为提高心血管功能计算机辅助诊断的可靠性,将粗糙集引入到心血管功能的计算机诊断中。通过对获取的参数进行统计分析,建立条件属性表、决策属性表和决策表;通过利用粗糙集相关矩阵,采用贪婪策略构造的寻找最小属性约简的启发式算法,对条件属性进行约简并建立相应的决策表和规则集。大量实例分析诊断表明,该方法确能提高机器辅助诊断心血管功能的正确性并且较容易实现。     

TED: Two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma

Microvascular invasion (MVI) has been clinically recognized as a prognostic factor for hepatocellular carcinoma (HCC) after surgical treatment. Detection of MVI before surgical operation greatly benefit patients’ prognosis and survival. Most of the existing methods for automatic diagnosis of MVI directly use deep neural networks to make predictions, which do not take into account clinical knowledge and lack of interpretability. To simulate the radiologists’ decision process, this paper proposes a Two-stage Expert-guided Diagnosis (TED) framework for MVI in HCC. Specifically, the first stage aims to predict key imaging attributes for MVI diagnosis, and the second stage leverages these predictions as a form of attention as well as soft supervision through a variant of triplet loss, to guide the fitting of the MVI diagnosis network. The attention and soft supervision are expected to jointly guide the network to learn more semantically correlated representations and thereafter increase the interpretability of the diagnosis network. Extensive experimental analysis on a private dataset of 466 cases has shown that the proposed method achieves 84.58% on AUC and 84.07% on recall, significantly exceeding the baseline methods.     

The impact of side effect framing on COVID-19 booster vaccine intentions in an Australian sample

ObjectiveTo evaluate the effect of presenting positively attribute-framed side effect information on COVID-19 booster vaccine intention relative to standard negatively-framed wording and a no-intervention control.Design and participantsA representative sample of Australian adults (N = 1204) were randomised to one of six conditions within a factorial design: Framing (Positive; Negative; Control) × Vaccine (Familiar (Pfizer); Unfamiliar (Moderna)).InterventionNegative Framing involved presenting the likelihood of experiencing side effects (e.g., heart inflammation is very rare, 1 in every 80,000 will be affected), whereas Positive Framing involved presenting the same information but as the likelihood of not experiencing side effects (e.g., 79,999 in every 80,000 will not be affected).Primary outcomeBooster vaccine intention measured pre- and post-intervention.ResultsParticipants were more familiar with the Pfizer vaccine (t(1203) = 28.63, p <.001, Cohen’s d_z = 0.83). Positive Framing (M = 75.7, SE = 0.9, 95% CI = [73.9, 77.4]) increased vaccine intention relative to Negative Framing (M = 70.7, SE = 0.9, 95% CI = [68.9, 72.4]) overall (F(1, 1192) = 4.68, p =.031, η_p² = 0.004). Framing interacted with Vaccine and Baseline Intention (F(2, 1192) = 6.18, p =.002, η_p² = 0.01). Positive Framing was superior, or at least equal, to Negative Framing and Control at increasing Booster Intention, irrespective of participants’ pre-intervention level of intent and vaccine type. Side effect worry and perceived severity mediated the effect of Positive vs. Negative Framing across vaccines.ConclusionPositive framing of side effect information appears superior for increasing vaccine intent relative to the standard negative wording currently used.Pre-registrationSee: aspredicted.org/LDX_2ZL.