2024-03-07 07:28:30
MedMamba: Vision Mamba for Medical Image Classification
Yubiao Yue, Zhenzhang Li
https://arxiv.org/abs/2403.03849 https://arxiv.org/p…
MedMamba: Vision Mamba for Medical Image Classification
Yubiao Yue, Zhenzhang Li
https://arxiv.org/abs/2403.03849 https://arxiv.org/p…
This https://arxiv.org/abs/2306.05494 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…
Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
Marwa Afnouch, Fares Bougourzi, Olfa Gaddour, Fadi Dornaika, Abdelmalik Taleb-Ahmed
https://arxiv.org/abs/2404.19598 https://arxiv.org/pdf/2404.19598
arXiv:2404.19598v1 Announce Type: new
Abstract: In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer vision and deep learning methods. This is particularly important in overcoming the challenges posed by diseases such as Bone Metastases (BM), a common and complex malignancy of the bones. Indeed, there have been an increasing interest in developing Machine Learning (ML) techniques into oncologic imaging for BM analysis. In order to provide a comprehensive overview of the current state-of-the-art and advancements for BM analysis using artificial intelligence, this review is conducted with the accordance with PRISMA guidelines. Firstly, this review highlights the clinical and oncologic perspectives of BM and the used medical imaging modalities, with discussing their advantages and limitations. Then the review focuses on modern approaches with considering the main BM analysis tasks, which includes: classification, detection and segmentation. The results analysis show that ML technologies can achieve promising performance for BM analysis and have significant potential to improve clinician efficiency and cope with time and cost limitations. Furthermore, there are requirements for further research to validate the clinical performance of ML tools and facilitate their integration into routine clinical practice.
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
Dan Zhao, Siddharth Samsi, Joseph McDonald, Baolin Li, David Bestor, Michael Jones, Devesh Tiwari, Vijay Gadepally
https://arxiv.org/abs/2402.18593
This https://arxiv.org/abs/2403.03849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…
This https://arxiv.org/abs/2403.03849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…
This https://arxiv.org/abs/2403.03849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…