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Titel:

Leveraging random forests for interactive exploration of large histological images.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Peter, Loïc; Mateus, Diana; Chatelain, Pierre; Schworm, Noemi; Stangl, Stefan; Multhoff, Gabriele; Navab, Nassir
Abstract:
The large size of histological images combined with their very challenging appearance are two main difficulties which considerably complicate their analysis. In this paper, we introduce an interactive strategy leveraging the output of a supervised random forest classifier to guide a user through such large visual data. Starting from a forest-based pixelwise estimate, subregions of the images at hand are automatically ranked and sequentially displayed according to their expected interest. After e...     »
Zeitschriftentitel:
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv
Jahr:
2014
Band / Volume:
17
Heft / Issue:
Pt 1
Seitenangaben Beitrag:
1-8
Sprache:
eng
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/25333094
Print-ISSN:
0302-9743
TUM Einrichtung:
Klinik und Poliklinik für RadioOnkologie und Strahlentherapie
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