Search Results for author: David Dov

Found 6 papers, 2 papers with code

RetiNerveNet: Using Recursive Deep Learning to Estimate Pointwise 24-2 Visual Field Data based on Retinal Structure

no code implementations15 Oct 2020 Shounak Datta, Eduardo B. Mariottoni, David Dov, Alessandro A. Jammal, Lawrence Carin, Felipe A. Medeiros

Due to the SAP test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field.

Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage

1 code implementation22 Jun 2020 Shijing Si, Rui Wang, Jedrek Wosik, Hao Zhang, David Dov, Guoyin Wang, Ricardo Henao, Lawrence Carin

Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.

Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes

1 code implementation12 Feb 2020 Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin

This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.

BIG-bench Machine Learning Computed Tomography (CT) +1

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

no code implementations29 Mar 2019 David Dov, Shahar Kovalsky, Jonathan Cohen, Danielle Range, Ricardo Henao, Lawrence Carin

We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images.

Multiple Instance Learning

Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection

no code implementations11 Apr 2016 David Dov, Ronen Talmon, Israel Cohen

In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences.

Action Detection Activity Detection +1

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