MedHelp: Enhancing medication compliance for demented elderly people with wearable visual intelligence

Dementia results in much stress in senior citizens and and immensely affects their quality of life. It also incurs huge financial and emotional burdens to their family members. Personal information assistance may alleviate such a problem by enhancing the sufferers’ ability to perceive the environment, understand their personal status, and make judgments and decisions. Leveraging on visual intelligence technologies, we develop a wearable system that provides personal assistance to enhance the independence and well-being of elderly people with dementia. An application called MedHelp was developed on Google Glass paired with a mobile phone to improve medication compliance. MedHelp has three functions: it sends out reminders to a user who is on regular medication; it helps a user to recognize the correct medicine container and provides dosing instructions; and it tracks the user’s medicine-taking activities so as to plan the time of next medications

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification.

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multi-target multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved fine-grained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images. FGDM helps to aggregate multi-layer robust information with wide range of scales to describe visually-similar overlapping defects. On the other hand, CDAM selects multiple representations of highly localized overlapping defect features and encodes the crucial spatial regions from discriminative channels to address variations in texture, viewing angle, shape and size of overlapping defect classes. Within iDAAM, FGDM and CDAM are interleaved to extract salient discriminative features from multiple scales by constructing an end-to-end trainable network without any preprocessing steps, making the process fully automatic. Experimental results and extensive ablation studies on three publicly available large concrete defect datasets show that our proposed approach outperforms the current state-of-the-art methodologies

Deep Regularized Discriminative Network

Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We propose an objective function which would utilize both the principal as well as nullspace eigenvalues and simultaneously inherit the class separability information onto its latent space representation. The idea is to build a convolutional neural network (CNN) and perform the regularized discriminant analysis on top of this and train it in an end-to-end fashion. The backpropagation is performed with a suitable optimizer to update the parameters so that the whole CNN approach minimizes the within class variance and maximizes the total class variance information suitable for both multi-class and binary class classification problems. Experimental results on four databases for multiple computer vision classification tasks show the efficacy of our proposed approach as compared to other popular methods

MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification

AbstractIn this work, we propose an attention-based deep convolutional neural network (CNN) model as an assistive computer-aided tool to classify common types of macular diseases: age-related macular degeneration, diabetic macular edema, diabetic retinopathy, choroidal neovascularization, macular hole, and central serous retinopathy from normal macular conditions with the help of scans from optical coherence tomography (OCT) imaging. Our proposed architecture unifies refined deep pre-trained models using transfer learning with limited training data and a deformation-aware attention mechanism encoding crucial morphological variations appearing in the deformation of retinal layers, detachments from the subsequent layers, presence of fluid-filled regions, geographic atrophy, scars, cysts, drusen, to achieve superior macular imaging classification performance. The proposed attention module facilitates the base network to automatically focus on the salient features arising due to the macular structural abnormalities while suppressing the irrelevant (or no cues) regions. The superiority of our proposed method lies in the fact that it does not require any pre-processing steps such as retinal flattening, denoising, and selection of a region of interest making it fully automatic and end-to-end trainable. Additionally, it requires a reduced number of network model parameters while achieving higher diagnostic performance. Extensive experimental results, analysis on four datasets along with the ablation studies show that the proposed architecture achieves state-of-the-art performance.</jats:p