Cerebral Cortex Extraction Methods Based on a Priori Knowledge for T1-Weighted MRI Images DOI
Hajer Ouerghi,

Olfa Mourali,

Ezzeddine Zagrouba

et al.

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 419 - 431

Published: Jan. 1, 2024

Language: Английский

Swin-UMamba: Mamba-Based UNet with ImageNet-Based Pretraining DOI
Jiarun Liu, Hao Yang, Hong-Yu Zhou

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 615 - 625

Published: Jan. 1, 2024

Language: Английский

Citations

48
Deep learning based MRI reconstruction with transformer DOI
Zhengliang L. Wu, Weibin Liao, Yan Chao

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2023, Volume and Issue: 233, P. 107452 - 107452

Published: March 1, 2023

Language: Английский

Citations

31
Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification DOI Creative Commons
Tariq Mahmood,

Amjad Rehman,

Tanzila Saba

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 113623 - 113652

Published: Jan. 1, 2023

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis decision-making, aiding intelligent services better disease management recovery. Due to unique nature images, algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, negatives. In view these problems, researchers primarily improve network structure but rarely from unstructured aspect. The paper tackles challenges, accentuating limitations convolutional neural network-based methods proposing solutions reduce annotation costs, particularly in complex introduces improvement strategies solve Additionally, article latest learning-based applications analysis, covering segmentation, acquisition, enhancement, registration, classification. Moreover, provides an overview four cutting-edge models, namely (CNN), belief (DBN), stacked autoencoder (SAE), recurrent (RNN). study selection involved searching benchmark academic databases, collecting relevant literature appropriate indicator emphasizing DL-based classification approaches, evaluating performance metrics. research highlights clinicians' scholars' obstacles developing efficient accurate malignancy prognostic framework state-of-the-art deep-learning algorithms. Furthermore, future perspectives explored overcome challenges advance field analysis.

Language: Английский

Citations

31
Artificial intelligence in four-dimensional imaging for motion management in radiation therapy DOI Creative Commons
Yinghui Wang,

Xiao Haonan,

Jing Wang

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

Citations

0
MCL: Multi-level Consistency Learning for Medical Image Segmentation DOI
Alou Diakite, Cheng Li, Lei Xie

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 32 - 41

Published: Jan. 1, 2025

Language: Английский

Citations

0
A critical assessment of artificial intelligence in magnetic resonance imaging of cancer DOI Creative Commons
Chengyue Wu, Meryem Abbad Andaloussi, David A. Hormuth

et al.

npj Imaging, Journal Year: 2025, Volume and Issue: 3(1)

Published: April 9, 2025

Given the enormous output and pace of development artificial intelligence (AI) methods in medical imaging, it can be challenging to identify true success stories determine state-of-the-art field. This report seeks provide magnetic resonance imaging (MRI) community with an initial guide into major areas which AI are contributing MRI oncology. After a general introduction intelligence, we proceed discuss successes current limitations when used for image acquisition, reconstruction, registration, segmentation, as well its utility assisting diagnostic prognostic settings. Within each section, attempt present balanced summary by first presenting common techniques, state readiness, clinical needs, barriers practical deployment setting. We conclude new advances must realized address questions regarding generalizability, quality assurance control, uncertainty quantification applying cancer maintain patient safety utility.

Language: Английский

Citations

0
Insular radiomic features associated with abnormal functional connectivity in individuals with nicotine addictions DOI
H. Zheng, Qianbiao Gu, Haobo Chen

et al.

The Journal of Smoking Cessation, Journal Year: 2025, Volume and Issue: 20(1), P. 0 - 0

Published: Jan. 1, 2025

Language: Английский

Citations

0
Development of a synthetic dataset generation method for deep learning of real urban landscapes using a 3D model of a non-existing realistic city DOI
Takuya Kikuchi, Tomohiro Fukuda, Nobuyoshi Yabuki

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 58, P. 102154 - 102154

Published: Aug. 31, 2023

Language: Английский

Citations

9
Model-based federated learning for accurate MR image reconstruction from undersampled k-space data DOI
Ruoyou Wu, Cheng Li, Juan Zou

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108905 - 108905

Published: July 27, 2024

Language: Английский

Citations

3
Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement DOI
Weijian Huang, Cheng Li, Hao Yang

et al.

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 97, P. 103299 - 103299

Published: Aug. 13, 2024

Language: Английский

Citations

3