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 …
Harnessing multimodal data integration to advance precision oncology
KM Boehm, P Khosravi, R Vanguri, J Gao… - Nature Reviews …, 2022 - nature.com
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
Brain tumor detection based on deep learning approaches and magnetic resonance imaging
AB Abdusalomov, M Mukhiddinov, TK Whangbo - Cancers, 2023 - mdpi.com
Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images
C Srinivas, NP KS, M Zakariah… - Journal of …, 2022 - Wiley Online Library
Brain tumor classification is a very important and the most prominent step for assessing life‐
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Dynamic-fusion-based federated learning for COVID-19 detection
Medical diagnostic image analysis (eg, CT scan or X-Ray) using machine learning is an
efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic …
efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic …
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher… - Proceedings of the …, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging …
beneficial for self-supervised learning schemes in computer vision and medical imaging …
Data augmentation for brain-tumor segmentation: a review
J Nalepa, M Marcinkiewicz, M Kawulok - Frontiers in computational …, 2019 - frontiersin.org
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
Timedistributed-cnn-lstm: A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study
S Montaha, S Azam, AKMRH Rafid, MZ Hasan… - IEEE …, 2022 - ieeexplore.ieee.org
Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely
diagnosis can increase the chances of survival. Considering the challenges of tumor …
diagnosis can increase the chances of survival. Considering the challenges of tumor …
[HTML][HTML] Application of artificial intelligence in 3D printing physical organ models
L Ma, S Yu, X Xu, SM Amadi, J Zhang, Z Wang - Materials Today Bio, 2023 - Elsevier
Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact
humanity. 3D printing of patient-specific organ models is expected to replace animal …
humanity. 3D printing of patient-specific organ models is expected to replace animal …