Results 81 to 90 of about 17,625 (278)

Limitations on Position Coding Imposed by Undersampling and Univariance

, 1996
Position judgments, which are exquisitely precise in the fovea, are markedly degraded in the periphery. In a recent article [Hess and Field 1993Vision Research, 33, 2663–2670] argue that the poor representation of positional information in peripheral ...
KLEIN, STANLEY A., DENNIS M. LEVI, LEVI, DENNIS M., STANLEY A. KLEIN   +3 more
core   +1 more source

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, EarlyView.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning

IEEE Access
Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly.
Haiou Qin
doaj   +1 more source

The undersampled discrete Gabor transform

IEEE Transactions on Signal Processing, 1998
Conventional studies on discrete Gabor transforms have generally been confined to the cases of critical sampling and oversampling in which the Gabor families span the whole signal space. In this paper, we investigate undersampled discrete Gabor transforms. For an undersampled Gabor triple (g,a,b), i.e.
openaire   +1 more source

Improving the Finite Sample Estimation of Average Treatment Effects Using Double/Debiased Machine Learning With Propensity Score Calibration

Journal of Applied Econometrics, EarlyView.
ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates.
Daniele Ballinari, Nora Bearth
wiley   +1 more source

Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized ¹²⁹Xe MRI Reconstruction. [PDF]

J Magn Reson Imaging
ABSTRACT Background Hyperpolarized 129Xe MRI faces technical challenges including low signal‐to‐noise ratio and breath‐hold constraints. Current literature focuses on proprietary deep learning methods or image‐domain enhancements. Purpose To present a comprehensive evaluation of transformer and hybrid CNN‐transformer architectures integrating dual ...
Babaeipour R, Fox MS, Parraga G, Ouriadov A.   +3 more
europepmc   +2 more sources

Fast Adaptive Undersampling for Volume Rendering

Journal of WSCG, 2019
Adaptive undersampling is a method for accelerating the rendering process by replacing the calculation of a volume integral with an interpolation procedure for a number of pixels. In this paper, we propose a method for accelerating the volume integral calculation for the rest of the pixels, i.e.
Belyaev, Sergey, Smirnov, Pavel, Smirnova, Natalia, Shubnikov, Vladislav   +3 more
openaire   +3 more sources

Comparison of Retrospective Motion Compensation Techniques for Pulmonary Dynamic Ultrashort Time to Echo MRI in Suspected Idiopathic Pulmonary Fibrosis

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Motion can degrade image quality during Ultrashort Time‐to‐Echo (UTE) pulmonary MRI and is particularly prevalent in patients with lung disease. Comprehensive assessment of the impact of motion compensation techniques on image quality and clinical interpretation is needed.
Abhilash S. Kizhakke Puliyakote, Luis A. Torres, Aiah AlAtoum, Natally AlArab, Kevin M. Johnson, Rodrigo M. Bello, Bryan O'Sullivan‐Murphy, Joseph G. Mammarappallil, Andrew D. Hahn, Sean B. Fain   +9 more
wiley   +1 more source

EKMGS: A HYBRID CLASS BALANCING METHOD FOR MEDICAL DATA PROCESSING

Scientific Journal of Astana IT University
The field of medicine is witnessing rapid development of AI, highlighting the importance of proper data processing. However, when working with medical data, there is a problem of class imbalance, where the amount of data about healthy patients ...
Zholdas Buribayev, Saida Shaikalamova, Ainur Yerkos, Rustem Imanbek   +3 more
doaj   +1 more source

Reviving Undersampling for Long-Tailed Learning

Pattern Recognition
The training datasets used in long-tailed recognition are extremely unbalanced, resulting in significant variation in per-class accuracy across categories. Prior works mostly used average accuracy to evaluate their algorithms, which easily ignores those worst-performing categories.
Hao Yu 0027, Yingxiao Du, Jianxin Wu 0001   +2 more
openaire   +2 more sources