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Found 17 papers, 8 papers with code
A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions
This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness, emphasizing the importance of early detection for effective treatment.
Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions
To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions.
Tomato Maturity Recognition with Convolutional Transformers
In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer.
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff.
An Incremental Learning Approach to Automatically Recognize Pulmonary Diseases from the Multi-vendor Chest Radiographs
Many researchers have utilized deep learning systems to diagnose pulmonary disorders using chest X-rays (CXRs).
Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats
Detection of illegal and threatening items in baggage is one of the utmost security concern nowadays.
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness.
Automated segmentation and extraction of posterior eye segment using OCT scans
This paper proposes an automated method for the segmentation and extraction of the posterior segment of the human eye, including the vitreous, retina, choroid, and sclera compartments, using multi-vendor optical coherence tomography (OCT) scans.
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Identifying potential threats concealed within the baggage is of prime concern for the security staff.
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images
Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues.
Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression
Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies.
Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
Detecting baggage threats is one of the most difficult tasks, even for expert officers.
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions
Retinal lesions play a vital role in the accurate classification of retinal abnormalities.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
In the last two decades, baggage scanning has globally become one of the prime aviation security concerns.
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression
In addition, the ocular traits among various biometric traits are preferably used in these recognition systems.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans
In the last two decades, luggage scanning has globally become one of the prime aviation security concerns.
