Results 81 to 90 of about 13,919 (263)

Undersampling techniques for large datasets

DNA-Encoded Libraries allow for an efficient approach to synthesize and screen billions of small molecules against a target of interest. With more real-world binding data, this can improve training of machine learning models. However, one key challenge in DELs is the severe imbalances between the classes, in other words, there are ...
Lexin Chen, Ramon Alain Miranda Quintana
openaire   +2 more sources

Position jitter and undersampling in pattern perception

Vision Research, 1999
The present paper addresses whether topographical jitter or undersampling might limit pattern perception in foveal, peripheral and strabismic amblyopic vision. In the first experiment, we measured contrast thresholds for detecting and identifying the orientation (up, down, left, right) of E-like patterns comprised of Gabor samples.
Levi, Dennis M, Klein, Stanley A, Sharma, Vineeta   +2 more
openaire   +2 more sources

Generative MR Multitasking With Complex‐Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real‐Time Imaging

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a unified image reconstruction framework that bridges real‐time and gated cardiac MRI, including quantitative MRI. Methods We introduce generative multitasking, which learns subject‐ and dataset‐specific implicit neural temporal bases from sequence timings and an interpretable latent space for cardiac and respiratory motion.
Xinguo Fang, Anthony G. Christodoulou
wiley   +1 more source

Efficient undersampling methods for highly imbalanced big data: PSU-m and PSU-mm

Machine Learning: Science and Technology
Data in the real world typically have disproportionate distribution, which makes it difficult to extract meaningful insights. In binary classification problems, often the number of instances in certain class dominates the others, making existing ...
Yongseok Jeon
doaj   +1 more source

Combined ADC and T2 Mapping in the Prostate Using a 3D Reduced FOV Sequence

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a 3D reduced FOV sequence for combined ADC and T2 mapping in the prostate in a single scan. Methods A 3D ADC and T2 mapping reduced FOV acquisition is enabled using T2 and diffusion preparation modules with slab‐selective tip‐down pulses and magnitude stabilizer gradients.
Yannik Ott, Sarah McElroy, Raphael Tomi‐Tricot, Carolyn Horst, Patricia A. Gutierrez, Rahul Khamar, Omar Darwish, Vicky Goh, Radhouene Neji   +8 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

Regularized Joint Reconstruction and Slab Combination for Accelerated Three‐Dimensional Multi‐Slab Diffusion‐Weighted Imaging Using Multi‐Scale Energy Models

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To jointly reconstruct high‐resolution diffusion‐weighted volumes and eliminate slab‐boundary artifacts while preserving fine anatomical detail from undersampled 3D multi‐slab k‐space acquisitions. Methods A bilinear forward model was formulated to describe the 3D multi‐slab acquisition, treating the image volume and slab excitation ...
Reza Ghorbani, Jyothi Rikhab Chand, Chu‐Yu Lee, Mathews Jacob, Merry Mani   +4 more
wiley   +1 more source

Undersampling and the measurement of beta diversity

Methods in Ecology and Evolution, 2013
Summary Beta diversity is a conceptual link between diversity at local and regional scales. Various additional methodologies of quantifying this and related phenomena have been applied. Among them, measures of pairwise (dis)similarity of sites are particularly popular. Undersampling, i.e.
Beck, Jan, Holloway, Jeremy D., Schwanghart, Wolfgang   +2 more
openaire   +3 more sources

Training Deep Learning Based Dynamic MR Image Reconstruction Using Synthetic Fractals

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets.
Anirudh Raman, Olivier Jaubert, Mark Wrobel, Tina Yao, Ruaraidh Campbell, Rebecca R Baker, Ruta Virsinskaite, Daniel Knight, Michael Quail, Jennifer A Steeden, Vivek Muthurangu   +10 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