4.6 Article

Improvement of Urinary Stone Segmentation Using GAN-Based Urinary Stones Inpainting Augmentation

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

SinGAN-Seg: Synthetic training data generation for medical image segmentation

Vajira Thambawita et al.

Summary: The study introduces a novel synthetic data generation pipeline SinGAN-Seg to produce synthetic medical images with corresponding masks using a single training image. The pipeline significantly improves the quality of generated data and enhances the performance of segmentation models when training datasets do not have a considerable amount of data.

PLOS ONE (2022)

Article Computer Science, Information Systems

Augmenting data with GANs to segment melanoma skin lesions

Federico Pollastri et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

Nabil Ibtehaz et al.

NEURAL NETWORKS (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Maciej A. Mazurowski et al.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2019)

Proceedings Paper Engineering, Biomedical

Using Synthetic Training Data for Deep Learning-Based GBM Segmentation

Lydia Lindner et al.

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)

Proceedings Paper Engineering, Biomedical

CLASS-AWARE ADVERSARIAL LUNG NODULE SYNTHESIS IN CT IMAGES

Jie Yang et al.

2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) (2019)

Article Geochemistry & Geophysics

Road Extraction by Deep Residual U-Net

Zhengxin Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

Article Computer Science, Artificial Intelligence

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Maayan Frid-Adar et al.

NEUROCOMPUTING (2018)

Article Computer Science, Information Systems

Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

Younghak Shin et al.

IEEE ACCESS (2018)

Proceedings Paper Computer Science, Theory & Methods

CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

Dakai Jin et al.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II (2018)

Proceedings Paper Computer Science, Theory & Methods

Conditional Infilling GANs for Data Augmentation in Mammogram Classification

Eric Wu et al.

IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES (2018)

Review Urology & Nephrology

Kidney Stone Disease: An Update on Current Concepts

Tilahun Alelign et al.

ADVANCES IN UROLOGY (2018)

Article Computer Science, Interdisciplinary Applications

Seamless Lesion Insertion for Data Augmentation in CAD Training

Aria Pezeshk et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2017)

Article Computer Science, Software Engineering

Globally and Locally Consistent Image Completion

Satoshi Iizuka et al.

ACM TRANSACTIONS ON GRAPHICS (2017)

Review Urology & Nephrology

An overview of kidney stone imaging techniques

Wayne Brisbane et al.

NATURE REVIEWS UROLOGY (2016)

Article Medicine, General & Internal

Kidney stones

Saeed R. Khan et al.

NATURE REVIEWS DISEASE PRIMERS (2016)

Article Engineering, Biomedical

Simulation and assessment of realistic breast lesions using fractal growth models

A. Rashidnasab et al.

PHYSICS IN MEDICINE AND BIOLOGY (2013)

Proceedings Paper Engineering, Electrical & Electronic

3D Lesion Insertion in Digital Breast Tomosynthesis Images

Michael S. Vaz et al.

MEDICAL IMAGING 2011: PHYSICS OF MEDICAL IMAGING (2011)

Article Radiology, Nuclear Medicine & Medical Imaging

Three-dimensional simulation of lung nodules for paediatric multidetector array CT

X. Li et al.

BRITISH JOURNAL OF RADIOLOGY (2009)

Article Computer Science, Artificial Intelligence

Linear-time connected-component labeling based on sequential local operations

K Suzuki et al.

COMPUTER VISION AND IMAGE UNDERSTANDING (2003)