Monitoring pulmonary rehabilitation and long-term oxygen therapy for people with chronic obstructive pulmonary disease (COPD)

This report outlines a proposed approach to monitoring access to, and utilisation of, pulmonary rehabilitation and long term oxygen therapy, by capitalising on existing data sources and identifying data development opportunities.SummaryChronic obstructive pulmonary disease (COPD) is a major cause of death and disability in Australia. About 1 in 13 people aged 40 and over have lung function consistent with a diagnosis of COPD. The disease develops over many years and therefore mainly affects middle-aged and older people. Smoking is its main, but not only, cause. Current clinical guidelines for the management of COPD (developed by the Thoracic Society of Australia and New Zealand and Lung Foundation Australia) emphasise the importance of care that encompasses both drug and non-drug based interventions designed to improve quality of life and survival. Pulmonary rehabilitation is a system of care that includes a combination of exercise, education and psychosocial support. It has been shown to have a wide range of beneficial effects, particularly because of its exercise component. Pulmonary rehabilitation implemented after a hospital admission reduces the risk of re-hospitalisation and death, and improves quality of life. Selective use of long-term oxygen therapy (LTOT)-the provision of supplemental oxygen therapy for 15 hours per day or more for people with COPD who have persistently low levels of oxygen in their blood-has been shown to improve quality of life and improve survival. Both of these therapies are among the key non-pharmacological interventions recommended in national and international clinical guidelines. Available evidence suggests, however, that pulmonary rehabilitation and LTOT are under-utilised in managing patients with COPD in Australia. The full extent of service provision, utilisation and under-utilisation is not known as there are no national data. This report outlines: proposed indicators relevant to monitoring access to, and utilisation of, pulmonary rehabilitation and LTOT in Australiaexisting data sources that may inform these indicatorsoptions for data developmentpotential challenges in monitoring these therapies. Improved information about access to, and use of, these interventions among people with COPD would enable: identification of opportunities for health improvementmeasurement of the benefits derived from these interventions. This would form a useful basis for data development to support assessment of the appropriateness of use, barriers to uptake and outcomes of these therapies. Similar information about the provision of non-inpatient, non-procedural and non- pharmaceutical therapies is also relevant to monitoring other chronic diseases in which these interventions improve quality of life and extend life. The authors of this report are Guy Marks, Helen Reddel, Elyse Guevara-Rattray, Leanne Poulos and Rosario Ampon of the Australian Centre for Asthma Monitoring (ACAM)

Exploring Food Detection using CNNs

One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. In this sense, building an automatic system for food analysis could allow a better understanding of the nutritional information with respect to the food eaten and thus it could help in taking corrective actions in order to consume a better diet. The Computer Vision community has focused its efforts on several areas involved in the visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results evidenced in the state of the art were obtained using Convolutional Neural Network. However, the results of all these different approaches were gotten on different datasets and therefore are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on GoogLeNet Convolutional Neural Network method, principal component analysis, and a support vector machine that outperforms the state of the art on two public food/non-food datasets

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

Lemon Classification Using Deep Learning

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach

Kernel discriminant analysis and clustering with parsimonious Gaussian process models

This work presents a family of parsimonious Gaussian process models which allow to build, from a finite sample, a model-based classifier in an infinite dimensional space. The proposed parsimonious models are obtained by constraining the eigen-decomposition of the Gaussian processes modeling each class. This allows in particular to use non-linear mapping functions which project the observations into infinite dimensional spaces. It is also demonstrated that the building of the classifier can be directly done from the observation space through a kernel function. The proposed classification method is thus able to classify data of various types such as categorical data, functional data or networks. Furthermore, it is possible to classify mixed data by combining different kernels. The methodology is as well extended to the unsupervised classification case. Experimental results on various data sets demonstrate the effectiveness of the proposed method