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Beloit College Student Research Symposium

Beloit College Student Research Symposium
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University Of Illinois College Medicine , United States , El Segundo , Royce Elementary School , Dong Nai , Rock Falls , Gig Harbor , Eagle River , Stevens Point , Bruxelles Capitale , Republic Of , Beloit College , Historic District , North Carolina , Northwestern University , Fort Worth , Ðn Ng , Island Lake , Poplar Grove , Door County , Brighton And Hove , United Kingdom , Siem Reap , Rock County , Daniela Padilla , Erik Henze ,

"Open-source, fully-automated hybrid cardiac substructure segmentation:" by Robert N. Finnegan, Vicky Chin et al.

Abstract: Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff ....

Breast Cancer , Ardiac Substructures , Deep Learning , Image Segmentation , Lung Cancer ,

Machine Learning Scientist job with Atomic Weapons Establishment (AWE)

Machine Learning Scientist job in Berkshire, England, Reading with Atomic Weapons Establishment (AWE). Apply Today. ....

United Kingdom , Opportunities For Professional Career Development , United Kingdom National , Group Personal Pension , Employee Assistance Programme , Occupational Health Services , Data Scientist , Machine Learning , People Promise , Professional Career Development , Assistance Programme , Occupational Health , Leave Policy , Machine Learning Scientist , Image Segmentation , Object Detection , Temporal Machine Learning Techniques , Temporal Machine Learning , Data Science ,

"Bayesian Gabor Network with Uncertainty Estimation for Pedestrian Lane" by Hoang Thanh Le, Son Lam Phung et al.

Automatic pedestrian lane detection is a challenging problem that is of great interest in assistive navigation and autonomous driving. Such a detection system must cope well with variations in lane surfaces and illumination conditions so that a vision-impaired user can navigate safely in unknown environments. This paper proposes a new lightweight Bayesian Gabor Network (BGN) for camera-based detection of pedestrian lanes in unstructured scenes. In our approach, each Gabor parameter is represented as a learnable Gaussian distribution using variational Bayesian inference. For the safety of vision-impaired users, in addition to an output segmentation map, the network provides two full-resolution maps of aleatoric uncertainty and epistemic uncertainty as well-calibrated confidence measures. Our Gabor-based method has fewer weights than the standard CNNs, therefore it is less prone to overfitting and requires fewer operations to compute. Compared to the state-of-the-art semantic segmentatio ....

Bayesian Gabor Network , Assistive And Autonomous Navigation , Bayes Methods , Bayesian Gabor Network , Image Segmentation , Lane Detection , Edestrian Lane Detection , Uncertainty Estimation , Variational Inference ,

"Cascaded deep learning-based auto-segmentation for head and neck cance" by James C. Korte, Nicholas Hardcastle et al.

Purpose: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI). Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto-segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk. Methods: Three convolutional neural network (CNN)-based auto-segmentation architectures were developed using manual segmentations and T2-weighted MRI images provided from the American Association of Physicists in Medicine (AAPM) radiotherapy MRI auto-contouring (RT-MAC) challenge dataset (n = 31). Auto-segmentation performance was evaluated with segmentation similarit ....

United States , American Association Of Physicists , American Association , Convolutional Neural Networks , Head And Neck Cancer , Image Segmentation , Magnetic Resonance Imaging , Organs At Risk ,