Machine Learning For Adaptation
Machine Learning for Adaptation is a computational framework within adaptive radiotherapy that employs machine learning algorithms to dynamically modify treatment plans in response to anatomical or physiological changes by learning from patient-specific longitudinal data.
13
Publications
370
Citations
48
Authors
40
Institutions
Publications per year
2017–2026
6
Parents
13
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
Zhiheng Cheng, Qingyue Wei, Hongru Zhu et al. · 2024 · 54 citations
1–5 of 13
48
1
h-index
1
h-index
Institute for High Performance Computing and Networking, Italy
1 publications · 18 citations
1
h-index
1
h-index
University of Electronic Science and Technology of China, China
1 publications · 26 citations
1
h-index
1–5 of 48
40
University of Michigan
Ann Arbor, United States
2 h-index
3 publications
76 citations
Stanford University
Stanford, United States
1 h-index
1 publications
54 citations
Tongji University
Shanghai, China
1 h-index
1 publications
26 citations

East China Normal University
Shanghai, China
1 h-index
2 publications
108 citations
Chan Zuckerberg Initiative (United States)
Redwood City, United States
1 h-index
1 publications
26 citations

Leibniz University Hannover
Hanover, Germany
1 h-index
1 publications
26 citations
University of Houston
Houston, United States
1 h-index
2 publications
68 citations
Research Center for Information Technology Innovation, Academia Sinica
Taipei, Taiwan
1 h-index
1 publications
19 citations
1–8 of 40
1 publications · 26 citations
1
h-index
1 publications · 36 citations
1
h-index
MSSP · 1 publications · 26 citations
1
h-index
1 publications · 10 citations
1
h-index
1 publications · 72 citations
1
h-index
1–5