computer-vision-10

Disease Prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Subtasks

Retinal OCT Disease Classification Disease Trajectory Forecasting

Most implemented papers

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

Ole-Christoffer Granmo, Kuruge Darshana Abeyrathna, M. G. Olsen, Xuan Zhang•Thu May 09 2019

A preprocessing method is introduced that extends the Tsetlin Machine so that it can handle continuous input so that dengue outbreak forecasts made by the TM are more accurate than those obtained by a Support Vector Machine, Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs).

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The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

S. Mukherjee, A. Eshaghi, A. Young, F. Barkhof, D. Alexander, S. Ourselin, Jiashi Feng, A. Toga, C. Davatzikos, Clémentine Fourrier, L. L. Rakêt, E. Bron, S. Klein, Razvan V. Marinescu, N. Oxtoby, M. Weiner, Nick C Fox, Tina Toni, Marcin Salaterski, V. Lunina, M. Ansart, S. Durrleman, Pascal Lu, S. Iddi, Dan Li, W. Thompson, M. Donohue, A. Nahon, Yarden Levy, Dan Halbersberg, M. Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose Gerardo Tamez-Peña, Aya Abdelsalam Ismail, Timothy Wood, H. C. Bravo, Minh Nguyen, Nanbo Sun, B. T. Yeo, Gan Chen, Kexin Qi, Shi-Yu Chen, D. Qiu, I. Buciuman, A. Kelner, R. Pop, Denisa Rimocea, M. Ghazi, M. Nielsen, Lauge Sørensen, Vikram Venkatraghavan, Keli Liu, C. Rabe, P. Manser, S. Hill, J. Howlett, Zhiyue Huang, S. Kiddle, Anaïs Rouanet, B. Taschler, B. Tom, S. White, N. Faux, S. Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, K. Estrada, L. Aksman, A. Altmann, C. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Aristeidis Sotiras, G. Erus, J. Doshi, J. Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, E. Jammeh, Igor Koval, P. Moore, Terry Lyons, J. Gallacher, J. Tohka, Robert Ciszek, B. Jedynak, K. Pandya, Murat Bilgel, W. Engels, Joseph Cole, P. Golland•Sat Feb 08 2020

Current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease, however, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical Trials.

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Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Huan Liu, Guansong Pang, Longbing Cao, Ling Chen•Tue Jun 12 2018

This paper introduces a ranking model-based framework, called RAMODO, that unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier Detection approach - the random distance-based approach.

233 0

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Naive Bayes and Text Classification I - Introduction and Theory

S. Raschka•Wed Oct 15 2014

The main concepts of naive Bayes classification in the context of document categorization are looked at.

180 0

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Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

D. Müller, Iñaki Soto Rey, F. Kramer•Thu Mar 25 2021

This work proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive capabilities of several heterogeneous deep convolutional neural network models.

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VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

A. Tavakkoli, Sharif Amit Kamran, Khondker Fariha Hossain, S. Zuckerbrod, K. Sanders, S. Baker•Tue Apr 13 2021

A novel conditional generative adversarial network (GAN) capable of simultaneously synthesizing FA images from fundus photographs while predicting retinal degeneration is proposed and exceeds recent state-of-the-art generative networks for fundus-to-angiography synthesis.

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Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

N. Navab, Chantal Pellegrini, Anees Kazi•Tue Mar 22 2022

The proposed graph based pre-training method helps in modeling the data at a population level and further improves performance on the fine tuning tasks in terms of AUC on average by 4.15% for MIMIC and 7.64% for TADPOLE.

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Heart Disease Prediction System using Associative Classification and Genetic Algorithm

M. Jabbar, B. L. Deekshatulu, Priti Chandra•Sat Mar 23 2013

Experimental Results show that most of the classifier rules help in the best prediction of heart disease which even helps doctors in their diagnosis decisions.

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Spectral Graph Convolutions for Population-based Disease Prediction

S. Ktena, D. Rueckert, Ben Glocker, Enzo Ferrante, M. J. Lee, Sarah Parisot, Ricardo Guerrero•Tue Mar 07 2017

This paper introduces the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data, and represents populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information.

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Robust Multiple Kernel k-means Clustering using Min-Max Optimization

Wei Wu, Seo-Jin Bang, Yaoliang Yu•Wed Feb 28 2018

This study shows that adversarial features added to a view can make the existing approaches with the min-max formulation in multiplekernel clustering yield unfavorable clusters, and proposes a multiple kernel clustering method with themin-max framework that aims to be robust to such adversarial perturbation.

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