Search Results for author:
Found 4 papers, 2 papers with code
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting
EPIC-Survival bridges encoding and aggregation into an end-to-end survival modelling approach, while introducing stratification boosting to encourage the model to not only optimize ranking, but also to discriminate between risk groups.
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary
Combinations of these clusters were significant in multivariate analysis.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
