An Application for Aesthetic Quality Assessment in Photography with Interpretability Features

Automatic aesthetic quality assessment is a computer vision problem in which we quantify the attractiveness or the appealingness of a photograph. This is especially useful in social networks, where the amount of images generated each day requires automation for processing. This work presents Aesthetic Selector, an application able to identify images of high aesthetic quality, showing also relevant information about the decisions and providing the use of the most appropriate filters to enhance a given image. We then analyzed the main proposals in the aesthetic quality field, describing their strengths and weaknesses in order to determine the filters to be included in the application Aesthetic Selector. This proposed application was tested, giving good results, in three different scenarios: image selection, image finding, and filter selection. Besides, we carried out a study of distinct visualization tools to better understand the models’ behavior. These techniques also allow detecting which areas are more relevant within the images when models perform classification. The application also includes this interpretability module. Aesthetic Selector is an innovative and original program, because in the field of aesthetic quality in photography, there are no applications that identify high-quality images and also because it offers the capability of showing information about which parts of the image have affected this decision.

Bilateral Internal Auditory Canal (IAC) Duplication with Congenital Sensorineural Hearing Loss (SNHL)-A Case Report

Duplication of the internal auditory canal is a rare entity. It can be unilateral or bilateral involvement and usually found during an investigation of sensorineural hearing loss or facial nerve palsy. We report this image finding in a child with Down syndrome with bilateral profound sensorineural hearing loss. There was no facial nerve palsy. We highlight the rarity of this image finding and the importance of imaging for detection of middle and inner ear abnormality in a case of congenital sensorineural hearing loss as it helps in decision making for the suitable treatment. Keywords: Duplicate IAC; Bilateral Duplicate IAC; sensorineural hearing loss; Down syndrome

An Efficient Damage Relief System based on Image Processing and Deep Learning Techniques

The Unmanned Aerial Vehicle (UAV) has been around for a long time but has been widely used recently by humans. Their acceptance of various communications-based applications is expected to improve coverage, compared to traditional ground-based solutions. In this paper, the Deep-learning and Image Processing Process framework is expected to provide solutions to the various problems already identified when UAVs are used for communication purposes. UAVs are used in disaster relief because of their accessibility even in inaccessible places. In this paper, we propose research into Deep learning and Image Processing strategies for UAVs. In deep learning is a form of machine learning that teaches computers to do what comes naturally to people: learn by example and get a lot of attention recently and for a good reason. It achieves previously impossible results. Image processing is the process of performing a specific task on an image, finding an enhanced image or extracting useful information from it. So our paper has the idea of using in depth face recognition and photo processing a digital photo taken by the UAV to identify victims of rescue, overcoming back to the latest UAV technology some of which include blurry images, unable to identify the victim when there are too many objects and much more. The solution includes a variety of features that allow for the distribution of images. It includes features and presentation of image detection and demonstrates the effectiveness of drone use in damage applications.

Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example

This positioning paper aims to discuss current challenges and opportunities for artificial intelligence (AI) in fungal lung disease, with a focus on chronic pulmonary aspergillosis and some supporting proof-of-concept results using lung imaging. Given the high uncertainty in fungal infection diagnosis and analyzing treatment response, AI could potentially have an impactful role; however, developing imaging-based machine learning raises several specific challenges. We discuss recommendations to engage the medical community in essential first steps towards fungal infection AI with gathering dedicated imaging registries, linking with non-imaging data and harmonizing image-finding annotations.

Individualized Treatment of Bone Marrow Edema of the Knee With the Aid of Classical Homeopathy: A Report of 2 Cases

Background: Bone marrow edema lesions around the knee are a common magnetic resonance image finding. They constitute a central component of a wide variety of inflammatory and noninflammatory conditions affecting the knee joint. Those lesions are not only a significant source of pain, but are also connected to many musculoskeletal pathologies. Case Report: We describe 2 cases of bone marrow edema of the knee joint treated with the aid of Classical Homeopathy. In both of the cases, Arnica montana was the chosen homeopathic remedy. Conclusions: The results of the treatment are promising, encouraging for more research on this area.

Practicing Justice, Justifying Practice: Toward Critical Practice Teacher Education

Arguments for social justice teacher education and arguments for practice-based teacher education are often seen as incongruous. Drawing on sociocultural theory and theories of justice, our study interrogates this underresearched assumption. We conducted video analyses of teacher education coursework and novice teachers’ K–6 classroom instruction, together with novices’ written reflections on videos. Data were collected during a university-based, accelerated teacher credentialing program. Analyses of videos of teacher education coursework revealed that while teacher educators frequently represented, decomposed, and approximated teaching practice, they rarely did so when discussing social justice issues. In a mirror-image finding, analyses of videos of (and reflections on) novices’ subsequent K–6 teaching revealed that novices rarely identified instructional decisions during which they attended to social justice issues.

The ‘Ivy Sign’ of Moyamoya disease

Stroke is uncommon in pediatric age group. Moyamoya is a rare idiopathic disease characterized by progressive vaso-occlusion of the main blood vessels to the brain. We report a case of 13 year old female who presented with recurrent headache and transient left hemiparesis and was diagnosed to have Moyamoya disease. The ‘ivy sign’ is a characteristic magnetic resonance image finding seen in patients with Moyamoya disease.