Search Results for author:
Found 16 papers, 12 papers with code
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology
However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e. g, SANS) for more precise disease identification.
SwinVFTR: A Novel Volumetric Feature-learning Transformer for 3D OCT Fluid Segmentation
To address these issues, we propose SwinVFTR, a new transformer-based architecture designed for precise fluid segmentation in 3D volumetric OCT images.
SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation
We also incorporate a novel embedding loss that calculates similarities between linear feature embeddings of the encoder and decoder blocks.
Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning
Therefore, for the assessment and management of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians.
Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases.
ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks
The experiment confirms that our model is more robust against such adversarial attacks for classifying arrhythmia with high accuracy.
ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks
Moreover, the model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples.
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers
The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA).
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
In order to avoid the loss of fidelity suffered by traditional GAN-based segmentation systems, we introduce a novel weighted feature matching loss.
Ranked #2 on
Retinal Vessel Segmentation
on STARE
Generative Adversarial Network
Retinal Vessel Segmentation
+2
Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters.
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.
Ranked #1 on
Retinal OCT Disease Classification
on Srinivasan2014
Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography
Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal.
Fundus to Angiography Generation
Generative Adversarial Network
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.
Ranked #1 on
Retinal OCT Disease Classification
on Srinivasan2014
AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018
Solving problems with Artificial intelligence in a competitive manner has long been absent in Bangladesh and Bengali-speaking community.
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide.
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
With the introduction of Fully Convolutional Neural Network, which uses finer strides and utilizes deconvolutional layers for upsampling, it has been a go to for any image segmentation task.
Ranked #1 on
Scene Segmentation
on NYU Depth v2
