Initiatives to reduce postoperative surgical site infections of the head and neck cancer surgery with a special emphasis on developing countries

Introduction: Surgery in patients with head and neck cancers is frequently complicated by multiple stages of procedure that includes significant surgical removal of all or part of an organ with cancer, tissue reconstruction, and extensive neck dissection. Postoperative wound infections, termed ‘surgical site infections’ are a significant impediment to head and neck cancer surgery and recovery, and need to be addressed. Areas Covered: Up to 10-45% of patients undergoing head-and-neck cancers surgery develop SSIs. SSIs can lead to delayed wound healing, increased morbidity and mortality as well as costs. Consequently, SSIs need to be avoided where possible, as even the surgery itself impacts on patients’ subsequent activities and their quality of life, which is exacerbated by SSIs. Several risk factors for SSIs need to be considered to reduce future rates, and care is also needed in the selection and duration of antibiotic prophylaxis. Expert commentary: Head and neck surgeons should give personalized care, especially to patients at high risk of SSIs. Such patients include those who have had chemoradiotherapy and need reconstructive surgery, and patients from lower and middle-income countries and from poorer communities in high income countries who often have high levels of co-morbidity because of resource constraints

Plant identification : man vs. machine LifeCLEF 2014 plant identification challenge

This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems

Multimedia in ecology

Pl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

Interactive plant identification based on social image data

Speeding up the collection and integration of raw botanical observation data is a crucial step towards a sustainable development of agriculture and the conservation of biodiversity. Initiated in the context of a citizen sciences project, the main contribution of this paper is an innovative collaborative workflow focused on image-based plant identification as a mean to enlist new contributors and facilitate access to botanical data. Since 2010, hundreds of thousands of geo-tagged and dated plant photographs were collected and revised by hundreds of novice, amateur and expert botanists of a specialized social network. An image-based identification tool - available as both a web and a mobile application - is synchronized with that growing data and allows any user to query or enrich the system with new observations. An important originality is that it works with up to five different organs contrarily to previous approaches that mainly relied on the leaf. This allows querying the system at any period of the year and with complementary images composing a plant observation. Extensive experiments of the visual search engine as well as system-oriented and user-oriented evaluations of the application show that it is already very helpful to determine a plant among hundreds or thousands of species. At the time of writing, the whole framework covers about half of the plant species living in France (2200 species), which already makes it the widest existing automated identification tool (with its imperfections)