4.6 Review

Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Improving fine-tuning of self-supervised models with Contrastive Initialization

Haolin Pan et al.

Summary: Self-supervised learning has made remarkable progress, but self-supervised models may not capture meaningful semantic information. To address this, we propose a Contrastive Initialization (COIN) method that introduces an extra class-aware initialization stage before fine-tuning. COIN significantly outperforms existing methods on multiple downstream tasks without introducing additional training cost.

NEURAL NETWORKS (2023)

Article Computer Science, Artificial Intelligence

A Comprehensive Survey of Image Augmentation Techniques for Deep Learning

Mingle Xu et al.

Summary: Although deep learning has achieved satisfactory performance in computer vision, collecting a large volume of images is often expensive and challenging. To alleviate this issue, many image augmentation algorithms have been proposed. This study provides a comprehensive survey of image augmentation for deep learning, introducing challenges in computer vision tasks and classifying algorithms into three categories: model-free, model-based, and optimizing policy-based. The survey enhances the understanding necessary for choosing suitable methods and designing novel algorithms.

PATTERN RECOGNITION (2023)

Article Computer Science, Artificial Intelligence

End-to-end learning for simultaneously generating decision map and multi-focus image fusion result

Boyuan Ma et al.

Summary: This paper proposes a cascade network to simultaneously generate decision map and fused result with an end-to-end training procedure, which avoids the dependence on empirical post-processing methods. It introduces a gradient aware loss function to preserve gradient information in output fused image and designs a decision calibration strategy to decrease the time consumption in the application of multiple images fusion.

NEUROCOMPUTING (2022)

Article Computer Science, Interdisciplinary Applications

Boundary learning by using weighted propagation in convolution network

Wei Liu et al.

Summary: This paper proposes a novel Weighted Propagation Convolution Neural Network based on U-Net (WPU-Net) for boundary detection in poly-crystalline microscopic images. By introducing spatial consistency and adaptive boundary weight, the method achieves promising performance in image segmentation.

JOURNAL OF COMPUTATIONAL SCIENCE (2022)

Proceedings Paper Computer Science, Artificial Intelligence

GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation

Xingzhe He et al.

Summary: This paper proposes a GAN-based approach that generates images conditioned on latent masks to alleviate the need for full or weak annotations in image segmentation. By learning mask-conditioned image generation in a hierarchical manner on 2D latent points, it increases robustness to viewpoint and object position changes and outperforms state-of-the-art unsupervised segmentation methods in training a segmentation network.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

Article Microscopy

Deep learning-based automatic inpainting for material microscopic images

Boyuan Ma et al.

Summary: Microscopic images are crucial for recording microstructure information of materials, but random damaged regions in these images can cause information loss and affect the accuracy of microstructural characterisation. To address this, a deep learning-based automatic method is provided for detecting and inpainting damaged regions, achieving promising performance for material microstructure characterisation.

JOURNAL OF MICROSCOPY (2021)

Article Computer Science, Artificial Intelligence

SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Boyuan Ma et al.

Summary: The study introduces an unsupervised deep learning model for multi-focus image fusion, achieving state-of-the-art fusion performance in objective and subjective assessments, especially in gradient-based fusion metrics.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

Article Medicine, General & Internal

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

Mohamed Elgendi et al.

Summary: Chest X-ray imaging technology is crucial for early detection of COVID-19, and deep learning methods can improve accuracy. Data augmentation helps reduce overfitting and enhance predictive accuracy on testing datasets.

FRONTIERS IN MEDICINE (2021)

Review Radiology, Nuclear Medicine & Medical Imaging

A review of medical image data augmentation techniques for deep learning applications

Phillip Chlap et al.

Summary: Data augmentation has become a popular method for training deep learning models in the field of radiology and radiotherapy, where limited medical image datasets are often encountered. By generating additional training data, data augmentation can improve model performance and has been widely used in state-of-the-art deep learning models.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI

Ruizhe Li et al.

Summary: This paper proposes a brain age estimation method based on generative adversarial network, which integrates a task-guided branch to improve model performance and achieves excellent results on a public dataset.

MEDICAL IMAGE UNDERSTANDING AND ANALYSIS (MIUA 2021) (2021)

Article Engineering, Biomedical

An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation

Siddharth Pandey et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2020)

Review Computer Science, Artificial Intelligence

A survey on face data augmentation for the training of deep neural networks

Xiang Wang et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

Article Computer Science, Artificial Intelligence

Greedy AutoAugment

Alireza Naghizadeh et al.

PATTERN RECOGNITION LETTERS (2020)

Article Chemistry, Physical

Data augmentation in microscopic images for material data mining

Boyuan Ma et al.

NPJ COMPUTATIONAL MATERIALS (2020)

Article Computer Science, Information Systems

Albumentations: Fast and Flexible Image Augmentations

Alexander Buslaev et al.

INFORMATION (2020)

Proceedings Paper Acoustics

PIXEL LEVEL DATA AUGMENTATION FOR SEMANTIC IMAGE SEGMENTATION USING GENERATIVE ADVERSARIAL NETWORKS

Shuangting Liu et al.

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (2019)

Article Computer Science, Artificial Intelligence

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Liang-Chieh Chen et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Article Multidisciplinary Sciences

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

Boyuan Ma et al.

SYMMETRY-BASEL (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Image-to-Image Translation with Conditional Adversarial Networks

Phillip Isola et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Review Materials Science, Multidisciplinary

Recent developments in advanced aircraft aluminium alloys

Tolga Dursun et al.

MATERIALS & DESIGN (2014)

Article Statistics & Probability

Comparing clusterings - an information based distance

Marina Meila

JOURNAL OF MULTIVARIATE ANALYSIS (2007)