Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network

Fujioka, Tomoyuki; Satoh, Yoko; Imokawa, Tomoki; Mori, M.; Yamaga, Emi; Takahashi, Kanae; Kubota, Kazunori; Oda, Hiroshi; Tateishi, Ukihide

doi:10.3390/diagnostics12123114

Cited by 5 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pix2Pix, introduced in 2016, is indeed a notable variant of GAN, specifically designed for image-to-image translation tasks [43][44][45][46]. It is particularly known for its use of an encoder-decoder architecture, which has been influential in various image conversion applications.…”

Section: Image Generationmentioning

confidence: 99%

Color Face Image Generation with Improved Generative Adversarial Networks

Chang,

Chung,

Chai

et al. 2024

Electronics

View full text Add to dashboard Cite

This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.

show abstract

Section: Image Generationmentioning

confidence: 99%

Color Face Image Generation with Improved Generative Adversarial Networks

Chang,

Chung,

Chai

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…GAN can be used to denoise medical images by learning to distinguish between true anatomical structures and noise or artifacts. Cleaner images can improve the accuracy of breast cancer detection algorithms by reducing false positives and enhancing the visibility of subtle abnormalities [65][66][67].…”

Section: Gan For Breast Cancer Detectionmentioning

confidence: 99%

Deep Learning Theories and Methods for Breast Cancer Classification

Wang

2023

Preprint

View full text Add to dashboard Cite

Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

show abstract