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"Clinical target volume delineation quality assurance for MRI-guided pr" by Hang Min, Jason Dowling et al.

Background and purpose: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. Materials and methods: The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framewor ....

Monte Carlo , Clinical Target Volume , Deep Learning , Prostate Cancer , Quality Assurance , Uncertainty Estimation ,

Fifth Youth Research Forum winners honoured

Doha:Qatar University Young Scientists Center (QU YSC) concluded the Fifth Youth Research Forum 2023, under the theme High Education Institutions and. ....

Ad Daw Ah , Ministry Of Education , Elyas Al Subhi , Ali Al Jabr Nuaimi , Aahid Al Hadrami , Hassan Al Derham , Zahra Al Mahrooqi , Mohammed Youssef Al Mulla , Omar Al Salmi , Tasneem El Sayed , Maria Nazir , Mariam Al Maadeed , Qu College Of Dental Medicine , Industrial Engineering Department , Institute Of Space Technology , Qatar Petrochemical Company , Department Of Electrical Engineering , College Of Education , Sultan Qaboos University , Youth Research Forum , College Of Science , Qatar University Young Scientists Center , Qu College Of Engineering , Their Role In Achieving Sustainable Development Goals , Fifth Youth Research Forum , High Education Institutions ,

Using Auto-Label and Uncertainty Estimation for Active Learning

Using Auto-Label and Uncertainty Estimation for Active Learning
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Tyler Mckean , Enterprise Data , Customer Success , Using Auto Label , Uncertainty Estimation , Active Learning ,

"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 ,