An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Drag Force Measurements of Vegetation Elements

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Families at Five: Extending Land-Grant Research Findings to Families

Families at Five is a joint community outreach partnership between Colorado State University (CSU) Department of Human Development and Family Studies and CSU Cooperative Extension. The program provides research-based family life education and resources to families, Extension educators, and family life community professionals. Comprised of an adult program with accompanying programs for adolescents and children, Families at Five is designed to educate family members on ways to strengthen family relationships. Included in the article are suggestions for engaging Cooperative Extension agents and other community practitioners in the program planning and delivery of educational programs

First Measurement of 72Ge(n,γ) at n_TOF

9th European Summer School on Experimental Nuclear AstrophysicsThe slow neutron capture process (s-process) is responsible for producing about half of the elemental abundances heavier than iron in the universo

Application of a sustainability framework to enhance Australian food literacy programs in remote Western Australian communities

Issue addressed: Food literacy programs aim to build individuals’ knowledge, skills and self-efficacy to adopt healthy food choices conducive to reducing the risk of chronic diseases, such as obesity. Foodbank WA’s (FBWA) Healthy Food for All ® nutrition programs have supported the improvement of food literacy knowledge and skills among vulnerable people living in the Pilbara. Methods: A Sustainability Framework containing ten sustainability factors was overlaid with social ecological model (SEM) levels of influence to form a matrix. The use of this matrix facilitated sustainability strategy appraisal within three food literacy programs delivered in remote WA. Results: Programs included multiple sustainability strategies across levels of influence; all programs addressed all ten sustainability factors at community and organisational SEM levels of influence. Few sustainability strategies were employed at the public policy level of influence. No program employed formal governance structures to guide program direction, such as steering groups; however, school and parent program staff developed Memoranda of Understanding to ensure the continuation of program delivery between the FBWA teams’ regional visits. Conclusions: This study has showcased the comprehensive assessment of food literacy program sustainability across levels of influence and identified gaps for improvement by FBWA teams. So What?: The sustainability of food literacy programs aiming to increase knowledge and skills could be enhanced by conducting a similar analysis, during program planning or at program review. Using the matrix provides the opportunity to focus resources to address sustainability; supporting health promotion practitioners to transform the impacts of short-term food literacy interventions into long-term sustained outcomes

Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study.

To investigate how various generations of iterative reconstruction (IR) algorithms impact low-contrast detectability (LCD) in abdominal computed tomography (CT) for different patient effective diameters, using a quantitative task-based approach. Investigations were performed using an anthropomorphic abdominal phantom with two optional additional rings to simulate varying patient effective diameters (25, 30, and 35 cm), and containing multiple spherical targets (5, 6, and 8 mm in diameter) with a 20-HU contrast difference. The phantom was scanned using routine abdominal protocols (CTDI <sub>vol</sub> , 5.9-16 mGy) on four CT systems from two manufacturers. Images were reconstructed using both filtered back-projection (FBP) and various IR algorithms: ASiR 50%, SAFIRE 3 (both statistical IRs), ASiR-V 50%, ADMIRE 3 (both partial model-based IRs), or Veo (full model-based IR). Section thickness/interval was 2/1 mm or 2.5/1.25 mm, except 0.625/0.625 mm for Veo. We assessed LCD using a channelized Hotelling observer with 10 dense differences of Gaussian channels, with the area under the receiver operating characteristic curve (AUC) as a figure of merit. For the smallest phantom (25-cm diameter) and smallest lesion size (5-mm diameter), AUC for FBP and the various IR algorithms did not significantly differ for any of the tested CT systems. For the largest phantom (35-cm diameter), Veo yielded the highest AUC improvement (8.5%). Statistical and partial model-based IR algorithms did not significantly improve LCD. In abdominal CT, switching from FBP to IR algorithms offers limited possibilities for achieving significant dose reductions while ensuring a constant objective LCD