Pattern Recognition
Overview
Pattern Recognition is a renowned interdisciplinary journal that focuses on the study and application of pattern recognition techniques. It covers a wide range of topics including machine learning, computer vision, data mining, and artificial intelligence. With its rigorous peer-review process, the journal provides a platform for researchers and practitioners to share their latest advancements and discoveries in this rapidly evolving field.
Details
Details
Abbr.
Pattern Recognit
Publisher
Elsevier
Start
1968
End
Continuing
Frequency
Monthly
p-ISSN
0031-3203
Country
United Kingdom
Language
English
Metrics
Metrics
h-index / Ranks: 392
245
SJR / Ranks: 721
2732
CiteScore / Ranks: 726
13.90
JIF / Ranks: 681
8.0
Recent Articles
1.
Zhuang Y, Li S, Shifat-E-Rabbi M, Yin X, Rubaiyat A, Rohde G
Pattern Recognit
. 2025 Mar;
162.
PMID: 40061222
We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon...
2.
Goldbraikh A, Shubi O, Rubin O, Pugh C, Laufer S
Pattern Recognit
. 2024 Nov;
156.
PMID: 39494221
Action segmentation is a challenging task in high-level process analysis, typically performed on video or kinematic data obtained from various sensors. This work presents two contributions related to action segmentation...
3.
Fang Y, Wu J, Wang Q, Qiu S, Bozoki A, Liu M
Pattern Recognit
. 2024 Sep;
157.
PMID: 39246820
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Existing...
4.
Barufaldi B, Gomes J, Filho T, do Rego T, Malheiros Y, Vent T, et al.
Pattern Recognit
. 2024 May;
153.
PMID: 38706638
The adoption of artificial intelligence (AI) in medical imaging requires careful evaluation of machine-learning algorithms. We propose the use of a "deep virtual clinical trial" (DeepVCT) method to effectively evaluate...
5.
Chen X, Liu Q, Deng H, Kuang T, Lin H, Xiao D, et al.
Pattern Recognit
. 2024 Apr;
152.
PMID: 38645435
Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically...
6.
Guan H, Yap P, Bozoki A, Liu M
Pattern Recognit
. 2024 Apr;
151.
PMID: 38559674
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve...
7.
Wei J, Wu Z, Wang L, Bui T, Qu L, Yap P, et al.
Pattern Recognit
. 2024 Mar;
124.
PMID: 38469076
Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using magnetic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T...
8.
Zhang L, Tanno R, Xu M, Huang Y, Bronik K, Jin C, et al.
Pattern Recognit
. 2023 Oct;
138:None.
PMID: 37781685
Supervised machine learning methods have been widely developed for segmentation tasks in recent years. However, the quality of labels has high impact on the predictive performance of these algorithms. This...
9.
Zhao C, Xu Z, Jiang J, Esposito M, Pienta D, Hung G, et al.
Pattern Recognit
. 2023 Jul;
143.
PMID: 37483334
Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis....
10.
Huang Y, Ahmad S, Han L, Wang S, Wu Z, Lin W, et al.
Pattern Recognit
. 2023 Jul;
143.
PMID: 37425426
Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired...