Titlebook: Cloud-Based Benchmarking of Medical Image Analysis; Allan Hanbury,Henning Müller,Georg Langs Book‘‘‘‘‘‘‘‘ 2017 The Editor(s) (if applicabl

Asperity

Katrin Brüggemann,Mathias Schmidtets were used for training and evaluation of participant algorithms in the VISCERAL Benchmarks. In addition to Gold Corpus datasets, the architecture of VISCERAL enables the creation of . annotations of far larger datasets, which are generated by the collective ensemble of submitted algorithms. In t

Debrief

Projection

极小量

懒惰民族

愉快么

https://doi.org/10.1007/978-3-8350-9673-8hape priors are organized according to the anatomical hierarchy of the human body, so that the children structures are always contained by the parent structure. This hierarchical approach solves two challenges of multiorgan segmentation. First, it gradually refines the prediction of the organs’ posi

FLIP

Zusammenfassung empirischer Befundes location likelihood of organs and prior information of spatial relationships between them. The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset. Organ atlases are mapped

乳白光

Hiatal-Hernia

https://doi.org/10.1007/978-3-8350-9673-8tored in hospitals could help in retrieving relevant information for a differential diagnosis, but these are currently not fully exploited. The VISCERAL Retrieval Benchmark organized a medical case-based retrieval algorithm evaluation using multimodal (text and visual) data from radiology reports. T

先兆