Results 41 to 50 of about 599,022 (331)
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
, 2018 We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations.
Kobayashi, Sosuke
core +1 more source
Data Augmentation via Lévy Processes [PDF]
, 2016 If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of invariances into a discriminative classifier. The framework imagines data as being drawn from a slice of a Levy process. If we slice the Levy process at an earlier point in time,
Wager, Stefan +2 more
openaire +2 more sources
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
BioData Mining, 2021 Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values.
Jacqueline Beinecke, Dominik Heider
doaj +1 more source
Data Augmentation for Skin Lesion Analysis
, 2018 Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited.
Avila, Sandra +3 more
core +1 more source
Style Augmentation: Data Augmentation via Style Randomization
, 2018 We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both classification and regression based tasks. During training, our style augmentation randomizes texture, contrast and color, while preserving shape and semantic content.
Jackson, Philip +4 more
openaire +3 more sources
Applied Sciences, 2022 Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets
Binbin Shi +5 more
doaj +1 more source
PanDA: Panoptic Data Augmentation [PDF]
, 2019 The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data
Liu, Yang +2 more
core +1 more source
, 2017 While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training.
Endo, Satoshi +7 more
core +1 more source
Data-Augmented Modeling of Intracranial Pressure [PDF]
Annals of Biomedical Engineering, 2019 Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement.
Jian-Xun Wang, Xiao Hu, Shawn C. Shadden
openaire +3 more sources
Parent‐to‐Child Information Disclosure in Pediatric Oncology
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite professional consensus regarding the importance of open communication with pediatric cancer patients about their disease, actual practice patterns of disclosure are understudied. Extant literature suggests a significant proportion of children are not told about their diagnosis/prognosis, which is purported to negatively ...
Rachel A. Kentor +12 more
wiley +1 more source