[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 …
Performance of machine learning for tissue outcome prediction in acute ischemic stroke: a systematic review and meta-analysis
X Wang, Y Fan, N Zhang, J Li, Y Duan… - Frontiers in neurology, 2022 - frontiersin.org
Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke
(AIS). This study aimed to provide a systematic review and meta-analysis of the overall …
(AIS). This study aimed to provide a systematic review and meta-analysis of the overall …
Segment anything model (sam) for radiation oncology
In this study, we evaluate the performance of the Segment Anything Model (SAM) model in
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …
Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy
Background Deep learning-based head and neck lymph node level (HN_LNL)
autodelineation is of high relevance to radiotherapy research and clinical treatment planning …
autodelineation is of high relevance to radiotherapy research and clinical treatment planning …
Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour …
In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or
both. However, navigating the multitude of available architectures for automatic …
both. However, navigating the multitude of available architectures for automatic …
Segvol: Universal and interactive volumetric medical image segmentation
Precise image segmentation provides clinical study with meaningful and well-structured
information. Despite the remarkable progress achieved in medical image segmentation …
information. Despite the remarkable progress achieved in medical image segmentation …
Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping 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 …