The PASCAL Visual Object Classes Challenge

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

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Deep Learning in Medical Image Analysis

This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.

Support vector machines: Theory and applications

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly’s daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Query2: Query over queries for improving gastrointestinal stromal tumour detection in an endoscopic ultrasound

Background Fluorescence angiography in colorectal surgery is a technique that may lead to lower anastomotic leak rates. However, the interpretation of the fluorescent signal is not standardised and there is a paucity of data regarding interobserver agreement. The aim of this study is to assess interobserver variability in selection of the transection point during fluorescence angiography before anastomosis. Methods An online survey with still images of fluorescence angiography was distributed through colorectal surgery channels containing images from 13 patients where several areas for transection were displayed to be chosen by raters. Agreement was assessed overall and between pre-planned rater cohorts (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists), using Fleiss’ kappa statistic. Results 101 raters had complete image ratings. No significant difference was found between raters when choosing a point of optimal bowel transection based on fluorescence angiography still images. There was no difference between pre-planned cohorts analysed (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists). Agreement between these cohorts was poor (<.26). Conclusion Whilst there is no learning curve for the technical adoption of FA, understanding the fluorescent signal characteristics is key to successful use. We found significant variation exists in interpretation of static fluorescence angiography data. Further efforts should be employed to standardise fluorescence angiography assessment.

https://discovery.ucl.ac.uk/id/eprint/10162749/2/Lovat_Interobserver%20assessment%20of%20colonic%20fluorescence%20angiography_manuscript.pdf

Interobserver Variability in the Assessment of Fluorescence Angiography in the Colon

Background: Fluorescence angiography has shown very promising results in reducing anastomotic leaks by allowing the surgeon to select optimally perfused tissue. However, subjective interpretation of the ﬂuorescent signal still hinders broad application of the technique, as signiﬁcant variation between diﬀerent surgeons exists. Our aim is to develop an artiﬁcial intelligence algorithm to classify colonic tissue as ’perfused’ or ’not perfused’ based on intraoperative ﬂuorescence angiography data. Methods: A classiﬁcation model with a Resnet architecture was trained on a dataset of ﬂuorescence angiography videos of colorectal resections at a ter-tiary referral centre. Frames corresponding to ﬂuorescent and non-ﬂuorescent segments of colon were used to train a classiﬁcation algorithm. Validation using frames from patients not used in the training set was performed, including both data collected using the same equipment and data collected using a dif-ferent camera. Performance metrics were calculated, and saliency maps used to further analyse the output. A decision boundary was identiﬁed based on the tissue classiﬁcation. Results: A convolutional neural network was successfully trained on 1790 frames from 7 patients and validated in 24 frames from 14 patients. The accuracy on the training set was 100%, on the validation set was 80%. Recall and precision were respectively 100% and 100% on the training set and 68.8% and 91.7% on the validation set. Conclusion: Automated classiﬁcation of intraoperative ﬂuorescence angiography with a high degree of accuracy is possible and allows automated decision boundary identiﬁcation. This will enable surgeons to standardise the technique of ﬂuorescence angiography. A web based app was made available to deploy the algorithm.

Fluorescence angiography classification in colorectal surgery - A preliminary report

Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occur in mono-chorionic multiple pregnancies due to placental vascular anastomoses. This procedure is particularly challenging due to limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to fluid turbidity, variability in light source, and unusual position of the placenta. This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. However, the research and development in this domain remain limited due to unavailability of high-quality data to encode the intra- and inter-procedure variability. Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos. In this paper, we provide an overview of the FetReg dataset, challenge tasks, evaluation metrics and baseline methods for both segmentation and registration. Baseline methods results on the FetReg dataset shows that our dataset poses interesting challenges, offering large opportunity for the creation of novel methods and models through a community effort initiative guided by the FetReg challenge.

FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset.

A method, system and computer readable medium comprising computer readable code for synchronising a plurality of videos, each video of the plurality of videos comprising a video element and an audio element, the method comprising obtaining features of one or more of the plurality of videos identifying a reference video to which a number of videos of the plurality of videos are to be synchronised, identifying the number of videos of the plurality of videos that are to be synchronised to the reference video; and synchronising in time the number of videos and the reference video based on the obtained features.

Method for clustering, synchronizing and compositing a plurality of videos corresponding to the same event. system and computer readable medium comprising computer readable code therefor

Sophia Bano

Papers

Query2: Query over queries for improving gastrointestinal stromal tumour detection in an endoscopic ultrasound

Interobserver Variability in the Assessment of Fluorescence Angiography in the Colon

Fluorescence angiography classification in colorectal surgery - A preliminary report

FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset.

Method for clustering, synchronizing and compositing a plurality of videos corresponding to the same event. system and computer readable medium comprising computer readable code therefor