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Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics. In this paper, we extend the use of reinforcement learning into controlling an ocular biomechanical system to perform saccades, which is one of the fastest eye movement systems. We describe an ocular environment and an agent trained using Deep Deterministic Policy Gradients method to perform saccades. The agent was able to match the desired eye position with a mean deviation angle of 3.5°±1.25°. The proposed framework is a first step towards using the capabilities of deep reinforcement learning to enhance our understanding of ocular biomechanics.

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Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and health care practitioners. Skin lesion restoration is an essential pre-processing step for lesion enhancements for accurate automated analysis and diagnosis by both dermatologists and computer-aided diagnosis tools. Hair occlusion is one of the most popular artifacts in dermatoscopic images. It can negatively impact the skin lesions diagnosis by both dermatologists and automated computer diagnostic tools. Digital hair removal is a non-invasive method for image enhancement for decrease the hair-occlusion artifact in previously captured images. Several hair removal methods were proposed for skin delineation and removal without standardized benchmarking techniques. Manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or against benchmarking datasets for comparison purposes. In the presented work, we propose a photo-realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with ground-truth mask for hair location. Hair-occluded image is synthesized using the latent structure of any input hair-free image by deep encoding the input image into a latent vector of features. The locations of required hair are highlighted using white pixels on the input image. Then, these deep encoded features are used to reconstruct the synthetic highly realistic hair-occluded image. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximize the image synthesis visual quality. For the evaluation of the generated samples, the t-SNE feature mapping and Bland-Altman test are used as visualization tools for the experimental results. The results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture. The proposed method can be used to generate benchmarking datasets for comparing the performance of digital hair removal methods. The code is available online at: https://github.com/attiamohammed/realhair.

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Prompt Gamma Neutron Activation Analysis is a nuclear-based technique that can be used in explosives detection. It relies on bombarding unknown samples with neutrons emitted from a neutron source. These neutrons interact with the sample nuclei emitting the gamma spectrum with peaks at specific energies, which are considered a fingerprint for the sample composition. Analyzing these peaks heights will give information about the unknown sample material composition. Shielding the sample from gamma rays or neutrons will affect the gamma spectrum obtained to be analyzed, providing a false indication about the sample constituents, especially when the shield is unknown. Here we show how using deep neural networks can solve the shielding drawback associated with the prompt gamma neutron activation analysis technique in explosives detection. We found that the introduced end-to-end framework was capable of differentiating between explosive and non-explosive hydrocarbons with accuracy of 95% for the previously included explosives in the model development data set. It was also, capable of generalizing with accuracy 80% over the explosives which were not included in the model development data set. Our results show that coupling prompt gamma neutron activation analysis with deep neural networks has a good potential for high accuracy explosives detection regardless of the shield presence.

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Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identification, speech recognition and problem solving. Artificial intelligence is now being increasingly studied for its potential uses in medicine. A sound understanding of the concepts of this emerging field is essential for the dermatologist as dermatology has abundant medical data and images that can be used to train artificial intelligence for patient care. There are already a number of artificial intelligence studies focusing on skin disorders such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article aims to present a basic introduction to the concepts of artificial intelligence as well as present an overview of the current research into artificial intelligence in dermatology, examining both its current applications and its future potential.

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Vergence-accommodation conflict (VAC) is the main contributor to visual fatigue during immersion in virtual environments. Many studies have investigated the effects of VAC using 3D displays and expensive complex apparatus and setup to create natural and conflicting viewing conditions. However, a limited number of studies targeted virtual environments simulated using modern consumer-grade VR headsets. Our main objective, in this work, is to test how the modern VR headsets (VR simulated depth) could affect our vergence system, in addition to investigating the effect of the simulated depth on the eye-gaze performance. The virtual scenario used included a common virtual object (a cube) in a simple virtual environment with no constraints placed on the head and neck movement of the subjects. We used ocular biomechanics and eye tracking to compare between vergence angles in matching (ideal) and conflicting (real) viewing conditions. Real vergence angle during immersion was significantly higher than ideal vergence angle and exhibited higher variability which leads to incorrect depth cues that affects depth perception and also leads to visual fatigue for prolonged virtual experiences. Additionally, we found that as the simulated depth increases, the ability of users to manipulate virtual objects with their eyes decreases, thus, decreasing the possibilities of interaction through eye gaze. The biomechanics model used here can be further extended to study muscular activity of eye muscles during immersion. It presents an efficient and flexible assessment tool for virtual environments.

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BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like regions as a pre-processing step for skin lesion images. Hair removal has two main steps: hair segmentation and hair gaps inpainting. However, hair segmentation is a challenging task which requires manual tuning of thresholding parameters. Hard-coded threshold leads to over-segmentation (false positives) which in return changes the textural integrity of lesions and or under-segmentation (false negatives) which leaves hair traces and artefacts which affect subsequent diagnosis. Additionally, dermal hair exhibits different characteristics: thin; overlapping; faded; occluded and overlaid on textured lesions. METHODS: In this presented paper, we proposed a deep learning approach based on a hybrid network of convolutional and recurrent layers for hair segmentation using weakly labelled data. We utilised the deep encoded features for accurate detection and delineation of hair in skin images. The encoded features are then fed into recurrent neural network layers to encode the spatial dependencies between disjointed patches. Experiments are conducted on a publicly available dataset, called "Towards Melanoma Detection: Challenge". We chose two metrics to evaluate the produced segmentation masks. The first metric is the Jaccard Index which penalises false positives and false negatives. The second metric is the tumour disturb pattern which assesses the overall effect over the lesion texture due to unnecessary inpainting as a result of over segmentation. The qualitative and quantitative evaluations are employed to compare the proposed technique with state-of-the-art methods. RESULTS: The proposed approach showed superior segmentation accuracy as demonstrated by a Jaccard Index of 77.8% in comparison to a 66.5% reported by the state-of-the-art method. We also achieved tumour disturb pattern as low as 14% compared to 23% for the state-of-the-art method. CONCLUSION: The hybrid architecture for segmentation was able to accurately delineate and segment the hair from the background including lesions and the skin using weakly labelled ground truth for training.

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Ensuring a healthier working environment is of utmost importance for companies and global health organizations. In manufacturing plants, the ergonomic assessment of adopted working postures is indispensable to avoid risk factors of work-related musculoskeletal disorders. This process receives high research interest and requires extracting plausible postural information as a preliminary step. This paper presents a semi-automated end-to-end ergonomic assessment system of adopted working postures. The proposed system analyzes the human posture holistically, does not rely on any attached markers, uses low cost depth technologies and leverages the state-of-the-art deep learning techniques. In particular, we train a deep convolutional neural network to analyze the articulated posture and predict body joint angles from a single depth image. The proposed method relies on learning from synthetic training images to allow simulating several physical tasks, different body shapes and rendering parameters and obtaining a highly generalizable model. The corresponding ground truth joint angles have been generated using a novel inverse kinematics modeling stage. We validated the proposed system in real environments and achieved a joint angle mean absolute error (MAE) of 3.19±1.57∘ and a rapid upper limb assessment (RULA) grand score prediction accuracy of 89% with Kappa index of 0.71 which means substantial agreement with reference scores. This work facilities evaluating several ergonomic assessment metrics as it provides direct access to necessary postural information overcoming the need for computationally expensive post-processing operations.

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Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. In this work, we are proposing a vehicle-based framework for kangaroo detection in urban and highway traffic environment that could be used for collision warning systems. Our proposed framework is based on region-based convolutional neural networks (RCNN). Given the scarcity of labeled data of kangaroos in traffic environments, we utilized our state-of-the-art data generation pipeline to generate 17,000 synthetic depth images of traffic scenes with kangaroo instances annotated in them. We trained our proposed RCNN-based framework on a subset of the generated synthetic depth images dataset. The proposed framework achieved a higher average precision (AP) score of 92% over all the testing synthetic depth image datasets. We compared our proposed framework against other baseline approaches and we outperformed it with more than 37% in AP score over all the testing datasets. Additionally, we evaluated the generalization performance of the proposed framework on real live data and we achieved a resilient detection accuracy without any further fine-tuning of our proposed RCNN-based framework.

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