Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting. [PDF]
Diagnostics (Basel), 2023 Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation.
Snider EJ +2 more
europepmc +2 more sources
YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation. [PDF]
Digit HealthObjective Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (
A Hasib U +5 more
europepmc +2 more sources
Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images. [PDF]
EBioMedicineSummary Background It has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise
Wang R +5 more
europepmc +2 more sources
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention.
Wangyu Wu +4 more
semanticscholar +1 more source
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification [PDF]
AAAI Conference on Artificial Intelligence, 2023 Existing image augmentation methods consist of two categories: perturbation-based methods and generative methods. Perturbation-based methods apply pre-defined perturbations to augment an original image, but only locally vary the image, thus lacking image
Bohan Li +6 more
semanticscholar +1 more source
Challenges of Adversarial Image Augmentations
CoRR, 2021 Image augmentations applied during training are crucial for the generalization performance of image classifiers. Therefore, a large body of research has focused on finding the optimal augmentation policy for a given task. Yet, RandAugment [2], a simple random augmentation policy, has recently been shown to outperform existing sophisticated policies ...
Arno Blaas +4 more
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Current Directions in Biomedical Engineering, 2021 Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data.
Wulff Daniel +3 more
doaj +1 more source
The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective. [PDF]
Front Med (Lausanne), 2021 Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies.
Elgendi M +13 more
europepmc +2 more sources
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification [PDF]
Neurocomputing, 2018 Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets.
Maayan Frid-Adar +5 more
semanticscholar +1 more source
Sensors, 2022 Data augmentation is an established technique in computer vision to foster the generalization of training and to deal with low data volume. Most data augmentation and computer vision research are focused on everyday images such as traffic data.
Mingkun Tan +2 more
doaj +1 more source