[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
Image data augmentation approaches: A comprehensive survey and future directions
Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …
vision tasks; however, the challenge of overfitting persists, especially when dealing with …
Geometric transformation-based data augmentation on defect classification of segmented images of semiconductor materials using a ResNet50 convolutional neural …
The emergence of machine learning (ML) and deep learning (DL) techniques opens a huge
opportunity for their implementation in industry. One of the tasks for which these techniques …
opportunity for their implementation in industry. One of the tasks for which these techniques …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Suboptimal generalization of machine learning models on unseen data is a key challenge
which hampers the clinical applicability of such models to medical imaging. Although …
which hampers the clinical applicability of such models to medical imaging. Although …
A survey of synthetic data augmentation methods in machine vision
A Mumuni, F Mumuni, NK Gerrar - Machine Intelligence Research, 2024 - Springer
The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …
neural network (CNN) models using large-scale image datasets that are representative of …
[PDF][PDF] Diffusion Model-Based Data Augmentation for Lung Ultrasound Classification with Limited Data.
Deep learning models typically require large quantities of data for good generalization.
However, acquiring labeled medical imaging data is expensive, particularly for rare …
However, acquiring labeled medical imaging data is expensive, particularly for rare …
Mixing signals: Data augmentation approach for deep learning based modulation recognition
X Xu, Z Chen, D Xu, H Zhou, S Yu, S Zheng… - arxiv preprint arxiv …, 2022 - arxiv.org
With the rapid development of deep learning, automatic modulation recognition (AMR), as
an important task in cognitive radio, has gradually transformed from traditional feature …
an important task in cognitive radio, has gradually transformed from traditional feature …
Research trends and applications of data augmentation algorithms
In the Machine Learning research community, there is a consensus regarding the
relationship between model complexity and the required amount of data and computation …
relationship between model complexity and the required amount of data and computation …
LeafNST: an improved data augmentation method for classification of plant disease using object-based neural style transfer
Plant diseases significantly threaten global agriculture, impacting crop yield and food
security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and …
security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and …