Results 61 to 70 of about 599,022 (331)
SeisAug: A data augmentation python toolkit
Applied Computing and GeosciencesA common limitation in applying any deep learning and machine learning techniques is the limited labelled dataset which can be addressed through Data augmentation (DA). SeisAug is a DA python toolkit to address this challenge in seismological studies. DA.
D. Pragnath +3 more
doaj +1 more source
Data augmentation scheme for federated learning with non-IID data
Tongxin xuebao, 2023 To solve the problem that the model accuracy remains low when the data are not independent and identically distributed (non-IID) across different clients in federated learning, a privacy-preserving data augmentation scheme was proposed.Firstly, a data ...
Lingtao TANG, Di WANG, Shengyun LIU
doaj +2 more sources
Conditional Data Synthesis Augmentation*
Journal of the American Statistical AssociationReliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges,
Xinyu Tian, Xiaotong Shen
openaire +2 more sources
Modeling hepatic fibrosis in TP53 knockout iPSC‐derived human liver organoids
Molecular Oncology, EarlyView.This study developed iPSC‐derived human liver organoids with TP53 gene knockout to model human liver fibrosis. These organoids showed elevated myofibroblast activation, early disease markers, and advanced fibrotic hallmarks. The use of profibrotic differentiation medium further amplified the fibrotic signature seen in the organoids.
Mustafa Karabicici +8 more
wiley +1 more source
A survey on Image Data Augmentation for Deep Learning
Journal of Big Data, 2019 Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very
Connor Shorten, Taghi M. Khoshgoftaar
doaj +1 more source
Adversarial Data Augmentation on Breast MRI Segmentation
Applied Sciences, 2021 The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs).
João F. Teixeira +5 more
doaj +1 more source
Molecular Oncology, EarlyView.This study integrates transcriptomic profiling of matched tumor and healthy tissues from 32 colorectal cancer patients with functional validation in patient‐derived organoids, revealing dysregulated metabolic programs driven by overexpressed xCT (SLC7A11) and SLC3A2, identifying an oncogenic cystine/glutamate transporter signature linked to ...
Marco Strecker +16 more
wiley +1 more source
Generative Cooperative Net for Image Generation and Data Augmentation
, 2018 How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired features.
Qin, Zengchang, Wan, Tao, Xu, Qiangeng
core +1 more source
How Tempering Fixes Data Augmentation in Bayesian Neural Networks [PDF]
, 2022 Gregor Bachmann +2 more
openalex +1 more source
Molecular Oncology, EarlyView.Pharmacologic ascorbate (vitamin C) increases ROS, disrupts cellular metabolism, and induces DNA damage in CRPC cells. These effects sensitize tumors to PARP inhibition, producing synergistic growth suppression with olaparib in vitro and significantly delayed tumor progression in vivo. Pyruvate rescue confirms ROS‐dependent activity.
Nicolas Gordon +13 more
wiley +1 more source