Recent advancements and future prospects in active deep learning for medical image segmentation and classification
Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
Deep learning based MRI reconstruction with transformer
Z Wu, W Liao, C Yan, M Zhao, G Liu, N Ma… - Computer Methods and …, 2023 - Elsevier
Magnetic resonance imaging (MRI) has become one of the most powerful imaging
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
Development of a synthetic dataset generation method for deep learning of real urban landscapes using a 3D model of a non-existing realistic city
In the urban landscaping field, training datasets for instance segmentation in the detection of
building facades are needed for complex analysis and simulation based on data. Manual …
building facades are needed for complex analysis and simulation based on data. Manual …
Deep Learning-Based Differential Diagnosis of Follicular Thyroid Tumors Using Histopathological Images
S Nojima, T Kadoi, A Suzuki, C Kato, S Ishida, K Kido… - Modern Pathology, 2023 - Elsevier
Deep learning systems (DLSs) have been developed for the histopathological assessment
of various types of tumors, but none are suitable for differential diagnosis between follicular …
of various types of tumors, but none are suitable for differential diagnosis between follicular …
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Lately, deep learning technology has been extensively investigated for accelerating
dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved …
dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved …
A modality‐collaborative convolution and transformer hybrid network for unpaired multi‐modal medical image segmentation with limited annotations
H Liu, Y Zhuang, E Song, X Xu, G Ma… - Medical …, 2023 - Wiley Online Library
Background Multi‐modal learning is widely adopted to learn the latent complementary
information between different modalities in multi‐modal medical image segmentation tasks …
information between different modalities in multi‐modal medical image segmentation tasks …
Parcel: physics-based unsupervised contrastive representation learning for multi-coil mr imaging
With the successful application of deep learning to magnetic resonance (MR) imaging,
parallel imaging techniques based on neural networks have attracted wide attention …
parallel imaging techniques based on neural networks have attracted wide attention …
Respiratory-correlated 4-dimensional magnetic resonance fingerprinting for liver cancer radiation therapy motion management
Purpose The objective of this study was to develop a respiratory-correlated (RC) 4-
dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF)(RC …
dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF)(RC …
BSANet: Boundary-aware and scale-aggregation networks for CMR image segmentation
The accurate segmentation of distinct cardiac regions from cardiac magnetic resonance
(CMR) images is pivotal for enhancing the diagnosis and prognosis of heart diseases …
(CMR) images is pivotal for enhancing the diagnosis and prognosis of heart diseases …
Machine learning for automatic Alzheimer's disease detection: addressing domain shift issues for building robust models
Alzheimer's disease (AD) is a type of brain disease that affects a person's ability to perform
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …