Results 51 to 60 of about 341,780 (320)

Advancements in Semi-Supervised Deep Learning for Brain Tumor Segmentation in MRI: A Literature Review

AI
For automatic tumor segmentation in magnetic resonance imaging (MRI), deep learning offers very powerful technical support with significant results. However, the success of supervised learning is strongly dependent on the quantity and accuracy of labeled
Chengcheng Jin, Theam Foo Ng, Haidi Ibrahim   +2 more
doaj   +1 more source

Semi-Supervised Learning by Augmented Distribution Alignment

, 2019
In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which ...
Li, Wen, Van Gool, Luc, Wang, Qin
core   +1 more source

Advancing Age Modulates Associations Between Cognitive Impairment and Brain Volumes in Early MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Cognitive impairment is common in multiple sclerosis (MS), but manifestations following the first demyelinating event are relatively unexplored. We investigated cross‐sectional associations between magnetic resonance imaging (MRI)–derived brain volumes and the presence of cognitive impairment outcomes five years after the first ...
Piriyankan Ananthavarathan, Marco Pitteri, Michael Foster, Sara Collorone, Sara Salama, Elisa Colato, Ferran Prados, Baris Kanber, Marios Yiannakas, Claudia A. M. Gandini Wheeler‐Kingshott, Indran Davagnanam, Frederik Barkhof, Declan Chard, Olga Ciccarelli, Ahmed Toosy   +14 more
wiley   +1 more source

SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
doaj   +1 more source

Geostatistical semi-supervised learning for spatial prediction

Artificial Intelligence in Geosciences, 2022
Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
doaj   +1 more source

Improving Landmark Localization with Semi-Supervised Learning

, 2018
We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class ...
Honari, Sina, Kautz, Jan, Molchanov, Pavlo, Pal, Christopher, Tyree, Stephen, Vincent, Pascal   +5 more
core   +1 more source

Lγ-PageRank for semi-supervised learning [PDF]

Applied Network Science, 2019
PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, particularly in cases of fuzzy graphs or unbalanced labeled data.
Esteban Bautista, Patrice Abry, Paulo Gonçalves   +2 more
openaire   +2 more sources

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Semi-supervised learning in cancer diagnostics

Frontiers in Oncology, 2022
In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making.
Jan-Niklas Eckardt, Jan-Niklas Eckardt, Martin Bornhäuser, Martin Bornhäuser, Martin Bornhäuser, Karsten Wendt, Karsten Wendt, Jan Moritz Middeke, Jan Moritz Middeke   +8 more
doaj   +1 more source

A semi-supervised spam mail detector [PDF]

, 2006
This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge.
Pfahringer, Bernhard
core   +2 more sources