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    End-to-end Adversarial Retinal Image Synthesis
    ( 2017) Costa,P ; Adrian Galdran ; Maria Inês Meyer ; Niemeijer,M ; Abramoff,M ; Ana Maria Mendonça ; Aurélio Campilho ; 6071 ; 6381 ; 6825 ; 6835
    In medical image analysis applications, the availability of large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a Generative Adversarial Network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality. IEEE
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    Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images
    ( 2018) Teresa Finisterra Araújo ; Ana Maria Mendonça ; Aurélio Campilho ; 6320 ; 6381 ; 6071
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    A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images
    ( 2018) Costa,P ; Adrian Galdran ; Smailagic,A ; Aurélio Campilho ; 6071 ; 6825
    Diabetic retinopathy (DR) detection is a critical retinal image analysis task in the context of early blindness prevention. Unfortunately, in order to train a model to accurately detect DR based on the presence of different retinal lesions, typically a dataset with medical expert's annotations at the pixel level is needed. In this paper, a new methodology based on the multiple instance learning (MIL) framework is developed in order to overcome this necessity by leveraging the implicit information present on annotations made at the image level. Contrary to previous MIL-based DR detection systems, the main contribution of the proposed technique is the joint optimization of the instance encoding and the image classification stages. In this way, more useful mid-level representations of pathological images can be obtained. The explainability of the model decisions is further enhanced by means of a new loss function enforcing appropriate instance and mid-level representations. The proposed technique achieves comparable or better results than other recently proposed methods, with 90% area under the receiver operating characteristic curve (AUC) on Messidor, 93% AUC on DR1, and 96% AUC on DR2, while improving the interpretability of the produced decisions.
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    Wearable Biomonitoring Platform for the Assessment of Stress and its Impact on Cognitive Performance of Firefighters: An Experimental Study
    ( 2018) Susana Cristina Rodrigues ; Joana Isabel Paiva ; Duarte Filipe Dias ; Pimentel,G ; Kaiseler,M ; João Paulo Cunha ; 6517 ; 5864 ; 6260 ; 6322
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    Stress among on-duty firefighters: an ambulatory assessment study
    ( 2018) Susana Cristina Rodrigues ; Joana Isabel Paiva ; Duarte Filipe Dias ; João Paulo Cunha ; 6260 ; 5864 ; 6517 ; 6322
    Background Stress at work has been broadly acknowledged as a worldwide problem and has been the focus of concern for many researchers. Firefighting, in particular, is frequently reported as a highly stressful occupation. In order to investigate firefighters’ occupational health in terms of stress events, perceptions, symptoms, and physiological reactions under real-world conditions, an ambulatory assessment protocol was developed. Methods Seventeen firefighters’ cardiac signal was continuously monitored during an average of three shifts within a working week with medical clinically certified equipment (VitalJacket®), which allows for continuous electrocardiogram (ECG) and actigraphy measurement. Psychological data were collected with a software application running on smartphones, collecting potential stressful events, stress symptoms, and stress appraisal. Results A total of 450.56 h of medical-quality ECG were collected, and heart rate variability (HRV) analysis was performed. Findings suggest that although ‘fire’ situations are more common, ‘accidents’ are more stressful. Additionally, firefighters showed high levels of physiological stress (based on AVNN and LF/HF HRV metrics) when compared to normative healthy population values that may not be diagnosed using merely self-reports. Discussion The proposed ambulatory study seems to be useful for the monitoring of stress levels and its potential impact on health of first responders. Additionally, it could also be an important tool for the design and implementation of efficient interventions and informed management resolutions in real time. Potential applications of this research include the development of quantified occupational health (qOHealth) devices for real life monitoring of emergency personnel stress reactions.