DYNAMIC INFORMATIVE PROPOSAL-BASED ITERATIVE TRAINING PROCEDURE FOR WEAKLY SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES

Dynamic Informative Proposal-Based Iterative Training Procedure for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) is an increasingly important task in remote sensing images.However, mainstream WSOD methods often rely on low-quality proposals due to the complex backgrounds of remote sensing Steam Pod images.Moreover, applying strong data augmentations directly in WSOD methods can introduce significant noise, which can h

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A Dedicated Veno-Venous Extracorporeal Membrane Oxygenation Unit during a Respiratory Pandemic: Lessons Learned from COVID-19 Part I: System Planning and Care Teams

Background: The most critically ill patients with coronavirus disease 2019 (COVID-19) may require advanced support modalities, such as veno-venous extracorporeal membrane oxygenation (VV-ECMO).A systematic, methodical approach to a respiratory pandemic on a state and institutional level is critical.Methods: We conducted retrospective review of our

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A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

Abstract To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value.A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors)

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