Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

Domaine:

healthcare

Type de record:

paper
Créateur:
TetRemMenZim
Hôte:avatar
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on this feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it out performs most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which out performs the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets. 9 pages

Visit

arxiv.org

Tasks

computer visionimage classification

Tags

Machine LearningComputer Vision and Pattern Recognition

Similaires

Deep Learning Architectures For Brain Vessel SegmentationTurmeric plant disease prediction using hybrid deep learning-based feature extraction and classification modelsDeep learning for automatic term extractionFeature Extraction Using Deep Learning Techniques to Identify Microplastics in Open Sewer SystemsDeep Visual Feature Learning for Person Re-identificationDeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

Deep Learning Architectures For Brain Vessel Segmentation

Deep Learning Architectures For Brain Vessel Segmentation

Poster presented at the Deep Learning Indaba 2022 by Khadija Iddrisu

Turmeric plant disease prediction using hybrid deep learning-based feature extraction and classification models

Abstract For many farmers, particularly in areas like Tamil Nadu and Andhra Prad

Deep learning for automatic term extraction

This thesis investigates how modern deep learning techniques can improve Automatic Term Extraction (

Feature Extraction Using Deep Learning Techniques to Identify Microplastics in Open Sewer Systems

Microplastics have been known to kill fish and other microorganisms that feed on them in water bodie

Deep Visual Feature Learning for Person Re-identification

Deep Visual Feature Learning for Person Re-identification

Poster presented at the Deep Learning Indaba 2022 by Mahmoud Ghorbrl

DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

A novel centerline extraction framework is reported which combines an end-to-end trainable multi-tas