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medical-2

Pupil Dilation

3260 papers • 126 benchmarks • 313 datasets

Phenomenon on which the human iris reacts to illumination changes and drugs; thus, changing the aperture of the pupil.

(Image credit: Papersgraph)

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Most implemented papers

The Sensorium competition on predicting large-scale mouse primary visual cortex activity

A. Tolias, Computer Engineering, Alexander S. Ecker, K. Willeke, Santiago A. Cadena, Paul G. Fahey, Mohammad Ali Bashiri, Laura Pede, Max F. Burg, Christoph M. Blessing, Zhiwei Ding, Konstantin-Klemens Lurz, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Fabian H. Sinz International Max Planck Research School f Systems, U. Tuebingen, H Germany, Institute for Bioinformatics, Medical Informatics, Germany Department of Materials Science, Campus Institute Data Science, U. Goettingen, Department of Neuroscience, Baylor College of Medicine, Houston, USA., Center for Clinical Neuroscience, Artificial Intelligence, I. F. Physics, Max Planck Institute for Dynamics, Self-Organization, Goettingen, Electrical, Rice University

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Thu Jun 16 2022

A large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dila-tion, and eye movements is collected.

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Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

A. Tolias, Alexander S. Ecker, Fabian H Sinz, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, E. Froudarakis, Dimitri Yatsenko, X. Pitkow, Jacob Reimer•Wed Oct 24 2018

A new deep recurrent network architecture is developed that predicts inferred spiking activity of thousands of mouse V1 neurons simulta-neously recorded with two-photon microscopy, while accounting for confounding factors such as the animal’s gaze position and brain state changes related to running state and pupil dilation.

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DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation

A. Czajka, Siamul Karim Khan, Patrick J. Tinsley•Sun Jul 17 2022

This paper proposes a novel deep autoencoder-based model that can effectively learn complex movements of iris texture features directly from the data, and demonstrates two immediate applications: better compensation for iri texture deformations in iris recognition algorithms, compared to linear models, and the creation of a generative algorithm that can aid human forensic examiners, who may need to compare iris images with a large difference in pupil dilation.

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Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation

Daniel P. Benalcazar, David A. Benalcazar, A. Valenzuela•Mon Oct 10 2022

The results indicate that the data augmentation method can improve segmentation accuracy up to 15% for images with high pupil dilation, which creates a more reliable iris recognition pipeline, even under extreme dilation.

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A flow-based latent state generative model of neural population responses to natural images

A. Tolias, Fabian H Sinz, Zhiwei Ding, Konstantin-Klemens Lurz, Taliah Muhammad, Edgar Y. Walker, Mohammad Bashiri, A. K. Jagadish, Zhuokun Ding•Thu Sep 09 2021

The model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability, and successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation.

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Pupil Dilation | State-of-the-Art