Deep Learning for the Radiographic Detection of periodontal Bone Loss

We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies

Body image distortions and muscle dysmorphia symptoms among Asian men : do exercise status and type matter?

Theoretical Framework: Body image distortions and muscle dysmorphia symptoms were assessed among 78 Asian men who engaged in regular resistance training, aerobic training or did not engage in either. Method: Body fat and muscularity were measured and participants also completed the Muscle Dysmorphia Disorder Inventory. Results: Resistance trained men selected a body shape ideal that was higher in muscularity and lower in body fat. Aerobically trained men also reported higher perceived current Body Fat even though their actual levels were close to their ideal. Conclusion: The results suggest that specificity in body image distortion (e.g., perceived current-ideal versus perceived current-actual) when examining body image distortions might reduce conflicting findings in extant research

Medium practices

In this essay I develop a topic addressed in my book, Film Art Phenomena: the question of medium specificity. Rosalind Krauss's essay 'Art In the Age of the Post-Medium Condition' has catalysed a move away from medium specificity to hybridity. I propose that questions of medium cannot be ignored, since they carry their own history and give rise to specific formal traits and possibilities. The research involves close critical analysis of four moving image works that have not previously been written about: two made with film, and one each with computer and mobile phone. The analyses are conducted by reference to my ideas about how technological peculiarities inform and inflect practice: I see the work's material composition, its form and final meaning as intricately bound up with each other. Film, video and the computer give rise to specific forms of moving image, partly because artists exploit a medium’s peculiarities, and because certain media lend themselves to some methodologies and not others. I do not seek hard distinctions between these media, but discuss them in terms of predispositions. For example, I discuss a 16mm cine film in which the shifting visibility of grain raises ideas around movement and stillness. The aim is to develop a definition of medium specificity, in relation to the moving image, that is not essentialist in the way previous versions were criticised for being, that is, based on ideas of "material substrate" (Wollen). I argue that film is a medium of stages, in contrast to the modern tapeless camcorder, in which all functions of recording, storage, playback and even editing are contained in a single device. Supported by a travel grant, I presented a version of this essay at the International Conference of Experimental Media Congress, Toronto, in April 2011, along with a selection of works: http://www.experimentalcongress.org/full-schedule

Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images

Images of the human retina vary considerably in their appearance depending on the skin pigmentation (amount of melanin) of the subject. Some form of normalisation of colour in retinal images is required for automated analysis of images if good sensitivity and specificity at detecting lesions is to be achieved in populations involving diverse races. Here we describe an approach to colour normalisation by shade-correction intra-image and histogram normalisation inter-image. The colour normalisation is assessed by its effect on the automated detection of microaneurysms in retinal images. It is shown that the Na¨ıve Bayes classifier used in microaneurysm detection benefits from the use of features measured over colour normalised images

Video surveillance for monitoring driver's fatigue and distraction

Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers