[HTML][HTML] Automated tumor segmentation in radiotherapy
Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and
to provide consistency across clinicians and institutions for radiation treatment planning …
to provide consistency across clinicians and institutions for radiation treatment planning …
Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives
The rapid development of artificial intelligence (AI) has gained importance, with many tools
already entering our daily lives. The medical field of radiation oncology is also subject to this …
already entering our daily lives. The medical field of radiation oncology is also subject to this …
Segment anything model (sam) for radiation oncology
L Zhang, Z Liu, L Zhang, Z Wu, X Yu, J Holmes… - ar** method PlantServation
R Akiyama, T Goto, T Tameshige, J Sugisaka… - Nature …, 2023 - nature.com
Long-term field monitoring of leaf pigment content is informative for understanding plant
responses to environments distinct from regulated chambers but is impractical by …
responses to environments distinct from regulated chambers but is impractical by …
[HTML][HTML] Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer
Introduction Emerging evidence suggests that data-driven support tools have found their
way into clinical decision-making in a number of areas, including cancer care. Improving …
way into clinical decision-making in a number of areas, including cancer care. Improving …
MS-TCNet: An effective Transformer–CNN combined network using multi-scale feature learning for 3D medical image segmentation
Y Ao, W Shi, B Ji, Y Miao, W He, Z Jiang - Computers in Biology and …, 2024 - Elsevier
Medical image segmentation is a fundamental research problem in the field of medical
image processing. Recently, the Transformer have achieved highly competitive performance …
image processing. Recently, the Transformer have achieved highly competitive performance …
UNet deep learning architecture for segmentation of vascular and non-vascular images: a microscopic look at UNet components buffered with pruning, explainable …
Biomedical image segmentation (BIS) task is challenging due to the variations in organ
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …