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Found 14 papers, 4 papers with code
Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
Indeed, there have been an increasing interest in developing Machine Learning (ML) techniques into oncologic imaging for BM analysis.
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
Inspired by the success of Transformers in Computer vision, Transformers have been widely investigated for medical imaging segmentation.
Ranked #9 on
Medical Image Segmentation
on Synapse multi-organ CT
Fair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation.
Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans
Since the emergence of Covid-19 in late 2019, medical image analysis using artificial intelligence (AI) has emerged as a crucial research area, particularly with the utility of CT-scan imaging for disease diagnosis.
Hand-Eye Calibration
In the light of this comparison, the non-linear optimization method, that solves for rotation and translation simultaneously, seems to be the most robust one with respect to noise and to measurement errors.
Visually Guided Object Grasping
In this paper we present a visual servoing approach to the problem of object grasping and more generally, to the problem of aligning an end-effector with an object.
D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation
The proposed D-TrAttUnet architecture is evaluated for both Binary and Multi-classes Covid-19 infection segmentation.
Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images
To develop generic and reliable approaches for diagnosing and assessing the severity of COVID-19 from chest X-rays (CXR), a large number of well-maintained COVID-19 datasets are needed.
2D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scans
CT-scan imaging is the most informative tool about this disease.
Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scan
On the other hand, we proposed an ensemble of Convolutional Layers with Inception models for Covid-19 severity detection.
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation
These grid-based methods produce a new style of image transformations using the dropping and fusing of information.
Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases.
Design, Implementation and Simulation of a Cloud Computing System for Enhancing Real-time Video Services by using VANET and Onboard Navigation Systems
The implemented architecture has been experimented in the case of a simulated road service to aid the police agency.
A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data
In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information.
