Neurology assessment by objective structured video examination (OSVE)

Summary
Background
The ability to carry out a neurological examination and make an appropriate differential diagnosis is one of the mainstays of our final Bachelor of Medicine (MB) exam; however, with the introduction of objective structured clinical examinations (OSCEs) it has become impossible to arrange for adequate numbers of suitable real patients to participate in the exam.

Context
It is vital that newly qualified doctors can perform a basic neurological examination, interpret the physical signs and formulate a differential diagnosis.

It is vital that newly qualified doctors can perform a basic neurological examination

Innovation
Since 2010 we have introduced an objective structured video examination (OSVE) of a neurological examination of a real patient as part of our final MB OSCE exam. The students view clips of parts of the examination process. They answer questions on the signs that are demonstrated and formulate a differential diagnosis.

Implications
This type of station is logistically a lot easier to organise than a large number of real patients at different examination sites. The featured patients have clearly demonstrated signs and, as every student sees the same patient, are perfectly standardised. It is highly acceptable to examiners and performed well as an assessment tool. There are, however, certain drawbacks in that we are not examining the student's examination technique or their interaction with the patient. Also, certain signs, in particular the assessment of muscle tone and power, are more difficult for a student to estimate in this situation

Recurrent Convolutional Network for Video-based Person Re-Identification

In this paper we propose a novel recurrent neural networkarchitecture for video-based person re-identification.Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture.Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.

https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID
Project Source Code

A comparison of endoscopy versus pathology sizing of colorectal adenomas and potential implications for surveillance colonoscopy

Background and AimsTo compare endoscopy and pathology sizing in a large population-based series of colorectal adenomas and to evaluate the implications for patient stratification into surveillance colonoscopy.MethodsEndoscopy and pathology sizes available from intact adenomas removed at colonoscopies performed as part of the Northern Ireland Bowel Cancer Screening Programme, from 2010 to 2015, were included in this study. Chi-squared tests were applied to compare size categories in relation to clinicopathological parameters and colonoscopy surveillance strata according to current American Gastroenterology Association and British Society of Gastroenterology guidelines.ResultsA total of 2521 adenomas from 1467 individuals were included. There was a trend toward larger endoscopy than pathology sizing in 4 of the 5 study centers, but overall sizing concordance was good. Significantly greater clustering with sizing to the nearest 5 mm was evident in endoscopy versus pathology sizing (30% vs 19%, p<0.001), which may result in lower accuracy. Applying a 10-mm cut-off relevant to guidelines on risk stratification, 7.3% of all adenomas and 28.3% of those 8 to 12 mm in size had discordant endoscopy and pathology size categorization. Depending upon which guidelines are applied, 4.8% to 9.1% of individuals had differing risk stratification for surveillance recommendations, with the use of pathology sizing resulting in marginally fewer recommended surveillance colonoscopies.ConclusionsChoice of pathology or endoscopy approaches to determine adenoma size will potentially influence surveillance colonoscopy follow-up in 4.8% to 9.1% of individuals. Pathology sizing appears more accurate than endoscopy sizing, and preferential use of pathology size would result in a small, but clinically important, decreased burden on surveillance colonoscopy demand. Careful endoscopy sizing is required for adenomas removed piecemeal.

Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.