Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Edge AI for early detection of chronic diseases and the spread of infectious diseases: opportunities, challenges, and future directions
E Badidi - Future Internet, 2023 - mdpi.com
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge
devices, is quickly becoming a critical component in early health prediction. Edge AI …
devices, is quickly becoming a critical component in early health prediction. Edge AI …
Dfu-siam a novel diabetic foot ulcer classification with deep learning
Diabetes affects roughly 537 million people in the world, and it is predicted to reach 783
million by 2045. Diabetic Foot Ulcer (DFU) is a major issue with diabetes that may lead to …
million by 2045. Diabetic Foot Ulcer (DFU) is a major issue with diabetes that may lead to …
Federated learning: A cross‐institutional feasibility study of deep learning based intracranial tumor delineation framework for stereotactic radiosurgery
Background Deep learning–based segmentation algorithms usually required large or multi‐
institute data sets to improve the performance and ability of generalization. However …
institute data sets to improve the performance and ability of generalization. However …
A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …
A review of medical diagnostic video analysis using deep learning techniques
The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy,
provides significant benefits in clinical practice by improving the efficiency and accuracy of …
provides significant benefits in clinical practice by improving the efficiency and accuracy of …
Fusion of textural and visual information for medical image modality retrieval using deep learning-based feature engineering
Medical image retrieval is essential to modern medical treatment because it enables doctors
to diagnose and treat a variety of illnesses. In this study, we present an innovative technique …
to diagnose and treat a variety of illnesses. In this study, we present an innovative technique …
BM-FL: A Balanced Weight Strategy for Multi-stage Federated Learning Against Multi-client Data Skewing
L Yuan, M Duan, G Xiao, Z Tang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) combined with Differential Privacy (DP) is widespread in
healthcare, finance, and IoT due to its advantages in multi-client data distribution. However …
healthcare, finance, and IoT due to its advantages in multi-client data distribution. However …
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Z Xu, M Xu, T Liao, Z Zheng, C Chen - arXiv preprint arXiv:2402.17202, 2024 - arxiv.org
Recently, the success of large models has demonstrated the importance of scaling up model
size. This has spurred interest in exploring collaborative training of large-scale models from …
size. This has spurred interest in exploring collaborative training of large-scale models from …
Role of Artificial Intelligence in Medical Image Analysis: A Review of Current Trends and Future Directions
X Li, L Zhang, J Yang, F Teng - Journal of Medical and Biological …, 2024 - Springer
Purpose This review offers insight into AI's current and future contributions to medical image
analysis. The article highlights the challenges associated with manual image interpretation …
analysis. The article highlights the challenges associated with manual image interpretation …