Generative adversarial networks in medical image augmentation: a review
Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …
image-based diagnosis and treatment models is increasing. Generative Adversarial …
A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
Deep neural networks for dental implant system classification
S Sukegawa, K Yoshii, T Hara, K Yamashita, K Nakano… - Biomolecules, 2020 - mdpi.com
In this study, we used panoramic X-ray images to classify and clarify the accuracy of different
dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning …
dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning …
The use of generative adversarial networks in medical image augmentation
A Makhlouf, M Maayah, N Abughanam… - Neural Computing and …, 2023 - Springer
Abstract Generative Adversarial Networks (GANs) have been widely applied in various
domains, including medical image analysis. GANs have been utilized in classification and …
domains, including medical image analysis. GANs have been utilized in classification and …
Towards machine learning-aided lung cancer clinical routines: Approaches and open challenges
Advancements in the development of computer-aided decision (CAD) systems for clinical
routines provide unquestionable benefits in connecting human medical expertise with …
routines provide unquestionable benefits in connecting human medical expertise with …
A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images
A Anaam, MA Al-antari, A Gofuku - Biomedical Signal Processing and …, 2023 - Elsevier
Background The identification of human epithelial type-2 mitotic cell patterns in the indirect
immunofluorescence images (IIF HEp-2) is a critical step for autoimmune diseases computer …
immunofluorescence images (IIF HEp-2) is a critical step for autoimmune diseases computer …
Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning
A Teramoto, Y Kiriyama, T Tsukamoto, E Sakurai… - Scientific reports, 2021 - nature.com
In cytological examination, suspicious cells are evaluated regarding malignancy and cancer
type. To assist this, we previously proposed an automated method based on supervised …
type. To assist this, we previously proposed an automated method based on supervised …
[PDF][PDF] Generative adversarial network based data augmentation to improve cervical cell classification model
S Yu, S Zhang, B Wang, H Dun, L Xu, X Huang… - Math. Biosci …, 2021 - aimspress.com
The survival rate of cervical cancer can be improved by the early screening. However, the
screening is a heavy task for pathologists. Thus, automatic cervical cell classification model …
screening is a heavy task for pathologists. Thus, automatic cervical cell classification model …
Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs
L Crespi, S Camnasio, D Dei, N Lambri… - arXiv preprint arXiv …, 2024 - arxiv.org
In many clinical settings, the use of both Computed Tomography (CT) and Magnetic
Resonance (MRI) is necessary to pursue a thorough understanding of the patient's anatomy …
Resonance (MRI) is necessary to pursue a thorough understanding of the patient's anatomy …