Aryan Raj

TL;DR: The Endoscopy Computer Vision Challenge (EndoCV) as discussed by the authors is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.

TL;DR: A summary of methods developed by the top 17 teams and an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentsation (EDD2020).

TL;DR: Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation.

TL;DR: In this article , the authors evaluated the performance of different machine learning algorithms for predicting customer churn in the context of a telecommunication service provider and found that XGBoost and CatBoost outperformed decision trees, logistic regression, K-Nearest Neighbor, Naive Bais and SVM.

TL;DR: This paper proposed a cross-attention layer in their model design to enable high-resolution synthesis using a convolution-based approach for conditioned inputs like text and bounding boxes, achieving state-of-the-art performance in text image synthesis, super-resolution and unconditional picture generation.