ArticleNeurocomputing2016
A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression.
Article in Neurocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A multistart tabu search-based method for feature selection in medical applications.Scientific reports · 2023Article
- Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection.Computational intelligence and neuroscience · 2021Review
- CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.IEEE transactions on medical imaging · 2020Article
- CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.Medical physics · 2020Article
- Deep learning-based three-dimensional segmentation of the prostate on computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.Medical physics · 2018Article
- A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images.Neurocomputing · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.