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Analysis of the hands in egocentric vision: A survey
Egocentric vision (a.k.a. first-person vision - FPV) applications have t...
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Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
A fall is an abnormal activity that occurs rarely, so it is hard to coll...
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Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling
Ultra low radiation dose in X-ray Computed Tomography (CT) is an importa...
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Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Knowledge-based planning (KBP) is an automated approach to radiation the...
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DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Human falls rarely occur; however, detecting falls is very important fro...
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A Survey of Mixed Data Clustering Algorithms
Most of the datasets normally contain either numeric or categorical feat...
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Prediction of New Onset Diabetes after Liver Transplant
25 within the next 5 years. These thousands of individuals are at 2-fold...
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Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies
Medical imaging machine learning algorithms are usually evaluated on a s...
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A Novel Initial Clusters Generation Method for K-means-based Clustering Algorithms for Mixed Datasets
Mixed datasets consist of numeric and categorical attributes. Various K-...
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Spatio-Temporal Adversarial Learning for Detecting Unseen Falls
Fall detection is an important problem from both the health and machine ...
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RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
COVID-19 spread across the globe at an immense rate has left healthcare ...
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