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 systematic review on data scarcity problem in deep learning: solution and applications
Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …
applications. Deep learning models require a large amount of data to train the model. In …
Medical image augmentation for lesion detection using a texture-constrained multichannel progressive GAN
Q Guan, Y Chen, Z Wei, AA Heidari, H Hu… - Computers in Biology …, 2022 - Elsevier
Lesion detectors based on deep learning can assist doctors in diagnosing diseases.
However, the performance of current detectors is likely to be unsatisfactory due to the …
However, the performance of current detectors is likely to be unsatisfactory due to the …
The Role of generative adversarial network in medical image analysis: An in-depth survey
M AlAmir, M AlGhamdi - ACM Computing Surveys, 2022 - dl.acm.org
A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …
the field of artificial intelligence, and its superior data generation capability has garnered …
Deep learning prostate MRI segmentation accuracy and robustness: a systematic review
Purpose To investigate the accuracy and robustness of prostate segmentation using deep
learning across various training data sizes, MRI vendors, prostate zones, and testing …
learning across various training data sizes, MRI vendors, prostate zones, and testing …
Ensembling with deep generative views
Recent generative models can synthesize" views" of artificial images that mimic real-world
variations, such as changes in color or pose, simply by learning from unlabeled image …
variations, such as changes in color or pose, simply by learning from unlabeled image …
Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke
Ischemic lesion segmentation and the time since stroke (TSS) onset classification from
paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is …
paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is …
Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences
Obtaining manual labels is time-consuming and labor-intensive on cardiac image
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations
Medical image segmentation has seen significant progress through the use of supervised
deep learning. Hereby, large annotated datasets were employed to reliably segment …
deep learning. Hereby, large annotated datasets were employed to reliably segment …
Integrated multi-omics with machine learning to uncover the intricacies of kidney disease
X Liu, J Shi, Y Jiao, J An, J Tian, Y Yang… - Briefings in …, 2024 - academic.oup.com
The development of omics technologies has driven a profound expansion in the scale of
biological data and the increased complexity in internal dimensions, prompting the …
biological data and the increased complexity in internal dimensions, prompting the …