computer-vision-2

severity prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in severity-prediction-2

Trend

Dataset

Best Model

Actions

Burned Area Delineation from Satellite Imagery

Libraries

i

Use these libraries to find severity-prediction-2 models and implementations

Datasets

AbuseAnalyzer Dataset

Burned Area Delineation from Satellite Imagery

Subtasks

Intubation Support Prediction

Most implemented papers

CVEfixes: automated collection of vulnerabilities and their fixes from open-source software

G. Bhandari, Amara Naseer, L. Moonen•Sun Jul 18 2021

This work proposes a method to automatically collect and curate a comprehensive vulnerability dataset from Common Vulnerabilities and Exposures records in the public National Vulnerability Database (NVD), and shares an initial release of the resulting vulnerability dataset named CVEfixes.

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Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

Guillaume-Alexandre Bilodeau, Imanne El Maâchi, W. Bouachir•Thu Oct 24 2019

Results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis.

231 0

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AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts

Manish Gupta, Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Shrivastava, P. Kumaraguru•Tue Sep 29 2020

A first of the kind dataset with 7,601 posts from Gab is presented which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior and a system to address these tasks is proposed.

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Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

Han Wu, Wenjie Ruan, Jiangtao Wang, Dingchang Zheng, Shaolin Li, Jian Chen, Kunwei Li, Xiangfei Chai, A. Helal•Tue Sep 29 2020

This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction, and identifies an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase.

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An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound

S. Fattah, Ankan Ghosh Dastider, Farhan Sadik•Sat Feb 27 2021

A frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames and is found to be very effective in detecting COVID-19 severity scores from LUS images.

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Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

P. Maes, U. Sarawgi, W. Zulfikar, Rishab Khincha•Fri Oct 02 2020

This work proposes an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's Dementia Severity and aims to encourage fair and aware models.

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Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data

Dimitris N. Metaxas, Ananya Jana, Carlos D Minacapelli, V. Rustgi•Thu Sep 09 2021

The underlying mathematical expression for the black-box models on COVID-19 severity prediction task is uncovered and the first to apply symbolic metamodeling to this task is applied, and important features and feature interactions are discovered.

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COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images

Wajid Arshad Abbasi, S. Abbas, S. Andleeb•Thu Dec 24 2020

An automatic CO VID-19 diagnostic and severity prediction (COVIDX) system that uses deep feature maps from CXR images to diagnose COVID-19 and its severity prediction and outperforms all the existing state-of-the-art methods designed for this purpose.

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Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

Luca Colomba, Elena Baralis, Simone Monaco, S. Greco, Alessandro Farasin, D. Apiletti, P. Garza, T. Cerquitelli•Sun Nov 21 2021

This work analyzes the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework and a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability.

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