Search Results for author:
Found 10 papers, 5 papers with code
Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images
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
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.
Fully Automated Tree Topology Estimation and Artery-Vein Classification
We also performed several ablation studies to separately verify the importance of the segmentation and AV labeling steps of our proposed method.
Optic Disc Segmentation using Disk-Centered Patch Augmentation
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.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.
Ranked #1 on
Caustics Segmentation
on SuperCaustics
Continual Learning with Deep Artificial Neurons
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.
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
With AIP, it is trivial to capture the same image under different conditions (e. g., fidelity, lighting, etc.)
Ranked #1 on
Depth Estimation
on DIODE
Deep Active Learning via Open Set Recognition
The goal of active learning is to infer the informativeness of unlabeled samples so as to minimize the number of requests to the oracle.
Dynamic Deep Networks for Retinal Vessel Segmentation
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.
Self-Net: Lifelong Learning via Continual Self-Modeling
We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights.
