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20172021
most citedOptimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors

49 citations · 81 across the 11 of their papers we have counts for

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Showing eess.IVShow all

5 papers · 1 filter

eess.IV20211 cited

AutoPtosis

Abdullah Aleem, Manoj Prabhakar Nallabothula, Pete Setabutr +2

Blepharoptosis, or ptosis as it is more commonly referred to, is a condition of the eyelid where the upper eyelid droops. The current diagnosis for ptosis involves cumbersome manua…

eess.IV2021

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications

Nooshin Mojab, Vahid Noroozi, Abdullah Aleem +8

Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to deve…

eess.IV20192 cited

Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study

Endre Grøvik, Darvin Yi, Michael Iv +9

The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI seque…

eess.IV20194 cited

MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation

Darvin Yi, Endre Grøvik, Michael Iv +9

Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep learning methods have been applied to segmentation tasks in medica…

eess.IV2019

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

Endre Grøvik, Darvin Yi, Michael Iv +3

Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study dem…