MMSRNet: Pathological image super-resolution by multi-task and multi-scale learning
X Wu, Z Chen, C Peng, X Ye - Biomedical Signal Processing and Control, 2023 - Elsevier
Pathological diagnosis is the gold standard for disease assessment in clinical practice. It is
conducted by inspecting the specimen at the microscopical level. Therefore, a very high …
conducted by inspecting the specimen at the microscopical level. Therefore, a very high …
Multimodal-boost: Multimodal medical image super-resolution using multi-attention network with wavelet transform
Multimodal medical images are widely used by clinicians and physicians to analyze and
retrieve complementary information from high-resolution images in a non-invasive manner …
retrieve complementary information from high-resolution images in a non-invasive manner …
Accurate and lightweight MRI super-resolution via multi-scale bidirectional fusion attention network
L Xu, G Li, Q Chen - Plos one, 2022 - journals.plos.org
High-resolution magnetic resonance (MR) imaging has attracted much attention due to its
contribution to clinical diagnoses and treatment. However, because of the interference of …
contribution to clinical diagnoses and treatment. However, because of the interference of …
Fast and accurate super-resolution of MR images based on lightweight generative adversarial network
H Li, Z Xuan, J Zhou, X Hu, B Yang - Multimedia Tools and Applications, 2023 - Springer
Single image super-resolution reconstruction (SISR) can effectively and economically
improve the spatial resolution of magnetic resonance (MR) images, and it helps more …
improve the spatial resolution of magnetic resonance (MR) images, and it helps more …
A super-resolution network using channel attention retention for pathology images
Image super-resolution (SR) significantly improves the quality of low-resolution images, and
is widely used for image reconstruction in various fields. Although the existing SR methods …
is widely used for image reconstruction in various fields. Although the existing SR methods …
FNSAM: Image super-resolution using a feedback network with self-attention mechanism
Y Huang, W Wang, M Li - Technology and Health Care, 2023 - content.iospress.com
BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich
pathological information which is of great significance in diagnosis and treatment of brain …
pathological information which is of great significance in diagnosis and treatment of brain …
PathSRGAN: multi-supervised super-resolution for cytopathological images using generative adversarial network
In the cytopathology screening of cervical cancer, high-resolution digital cytopathological
slides are critical for the interpretation of lesion cells. However, the acquisition of high …
slides are critical for the interpretation of lesion cells. However, the acquisition of high …
Single image super-resolution: from discrete to continuous scale without retraining
Convolutional neural network (CNN)-based single image super-resolution (SR) methods
have achieved superior performance on some discrete-scaling factors, including 2, 3, and 4 …
have achieved superior performance on some discrete-scaling factors, including 2, 3, and 4 …
MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical Images
Existing super-resolution (SR) models require separate training for different scales of SR
because of the fixed upsampling levels in their architecture. We propose a novel one-time …
because of the fixed upsampling levels in their architecture. We propose a novel one-time …
Csrgan: medical image super-resolution using a generative adversarial network
Super-resolution medical image is vital for doctor's diagnosis and quantitative analysis. In
this work we propose a novel super-resolution generative adversarial network which …
this work we propose a novel super-resolution generative adversarial network which …