Search Results for author: Rolando Estrada

Found 10 papers, 5 papers with code

Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images

no code implementations4 Jan 2023 Aashis Khanal, Rolando Estrada

This paper is a follow-up paper on vessel topology estimation and extraction, we use the extracted topology to perform A-V state-of-the-art Artery-Vein classification, AV ratio calculation, and vessel tortuosity measurement, all fully automated.

Deep Active Learning Using Barlow Twins

no code implementations30 Dec 2022 Jaya Krishna Mandivarapu, Blake Camp, Rolando Estrada

In this paper, we unify these two families of approaches from the angle of active learning using self-supervised learning mainfold and propose Deep Active Learning using BarlowTwins(DALBT), an active learning method for all the datasets using combination of classifier trained along with self-supervised loss framework of Barlow Twins to a setting where the model can encode the invariance of artificially created distortions, e. g. rotation, solarization, cropping etc.

Active Learning Self-Supervised Learning

Fully Automated Tree Topology Estimation and Artery-Vein Classification

no code implementations4 Feb 2022 Aashis Khanal, Saeid Motevali, Rolando Estrada

We also performed several ablation studies to separately verify the importance of the segmentation and AV labeling steps of our proposed method.

Anatomy Classification

Optic Disc Segmentation using Disk-Centered Patch Augmentation

no code implementations1 Oct 2021 Saeid Motevali, Aashis Khanal, Rolando Estrada

The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases.

Optic Disc Segmentation

SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications

1 code implementation23 Jul 2021 Mehdi Mousavi, Rolando Estrada

In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.

Caustics Segmentation Depth Completion +6

Continual Learning with Deep Artificial Neurons

no code implementations13 Nov 2020 Blake Camp, Jaya Krishna Mandivarapu, Rolando Estrada

We demonstrate that it is possible to meta-learn a single parameter vector, which we dub a neuronal phenotype, shared by all DANs in the network, which facilitates a meta-objective during deployment.

Continual Learning

Deep Active Learning via Open Set Recognition

1 code implementation4 Jul 2020 Jaya Krishna Mandivarapu, Blake Camp, Rolando Estrada

The goal of active learning is to infer the informativeness of unlabeled samples so as to minimize the number of requests to the oracle.

Active Learning Informativeness +1

Dynamic Deep Networks for Retinal Vessel Segmentation

4 code implementations19 Mar 2019 Aashis Khanal, Rolando Estrada

To address this limitation, we propose a novel, stochastic training scheme for deep neural networks that better classifies the faint, ambiguous regions of the image.

Retinal Vessel Segmentation

Self-Net: Lifelong Learning via Continual Self-Modeling

1 code implementation25 May 2018 Blake Camp, Jaya Krishna Mandivarapu, Rolando Estrada

We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights.

Continual Learning

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