ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education

2025-05-31 · Ruiming Min, Minghao Liu

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, particularly generative models, presents a promising solution. These models can create diverse and comparable imaging datasets without compromising patient privacy, thereby supporting modern medical education. In this study, we explore the use of convolutional neural networks (CNNs) and CycleGAN (Zhu et al., 2017) for generating synthetic medical images. The source code is available at https://github.com/mliuby/COMP4211-Project.

📄 PDF Abstract BibTeX arXiv:2506.00605

Code (1)

mliuby/comp4211-project 공식 구현 pytorch

Tasks

Diagnostic

An Example for Domain Adaptation Using CycleGAN

2026-01-13 · Yanhua Zhao arxiv

Cycle-Consistent Adversarial Network (CycleGAN) is very promising in domain adaptation. In this report, an example in medical domain will be explained. We present struecture of a CycleGAN model for unpaired image-to-imag…

Image-to-Image Translation Domain Adaptation

Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images

2020-09-10 · Julian Alberto Palladino, Diego Fernandez Slezak, Enzo Ferrante

Automatic segmentation of white matter hyperintensities in magnetic resonance images is of paramount clinical and research importance. Quantification of these lesions serve as a predictor for risk of stroke, dementia and…

Domain Adaptation Image Segmentation Segmentation Semantic Segmentation+1

Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification

2021-04-03 · Saurabh Kataria, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velázquez 외

With the increase in the availability of speech from varied domains, it is imperative to use such out-of-domain data to improve existing speech systems. Domain adaptation is a prominent pre-processing approach for this. …

Domain Adaptation Speaker Verification Translation

MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry

2020-07-15 · Cedric Schockaert, Henri Hoyez

In the current era, an increasing number of machine learning models is generated for the automation of industrial processes. To that end, machine learning models are trained using historical data of each single asset lea…

BIG-bench Machine Learning Domain Adaptation Time Series Time Series Analysis

Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs

2022-10-28 · Reo Yoneyama, Ryuichi Yamamoto, Kentaro Tachibana

Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the acoustic characteristics of the input data…

Audio Super-Resolution Domain Adaptation Super-Resolution