By Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk
This ebook constitutes the refereed lawsuits of the seventh overseas Workshop on laptop studying in scientific Imaging, MLMI 2016, held at the side of MICCAI 2016, in Athens, Greece, in October 2016.
The 38 complete papers awarded during this quantity have been rigorously reviewed and chosen from 60 submissions.
The major objective of this workshop is to aid develop clinical examine in the extensive box of computer studying in clinical imaging. The workshop specializes in significant traits and demanding situations during this sector, and offers works aimed to spot new state of the art options and their use in scientific imaging.
Read Online or Download Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings PDF
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This publication constitutes the refereed complaints of the seventh overseas Workshop on computing device studying in clinical Imaging, MLMI 2016, held along side MICCAI 2016, in Athens, Greece, in October 2016. The 38 complete papers awarded during this quantity have been rigorously reviewed and chosen from 60 submissions.
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Extra resources for Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings
Color ﬁgure online) Building an Ensemble of Complementary Segmentation Methods 29 method. A related work by Ledig et al.  proposed to reﬁne the results of label fusion by using intensity-based models. Such reﬁnement was based on the variation of intensity distributions between the reﬁned and non-reﬁned segmentations. This heuristic is suitable for correcting errors that produce large variations in the intensity distributions (typically, merging csf-like with non-csf-like structures), whereas our method deals with learning a systematic combination of an arbitrary set of methods which is optimal given the set of available atlases.
Imaging 32(7), 1153–1190 (2013) 12. : Image matching as a diﬀusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998) 13. : The POPI-model, a pointvalidated pixel-based breathing thorax model. In: International Conference on the Use of Computers in Radiation Therapy, vol. 2, pp. 195–199 (2007) 14. : Total variation regularization of displacements in parametric image registration. , Saini, S. ) ABDI 2014. LNCS, vol. 8676, pp. 211–220. Springer, Heidelberg (2014) Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging?
Yij fr xij ≥ 1 − ξij minfr reg (fr ) + C j (1) i∈Γr where reg (·) is the regularization term penalizing highly complex functions, ξij indicates the error incurred by each training sample and C controls the trade-oﬀ between data ﬁtting and regularization. In the testing phase, the (crisp) label at position i ∈ Γr on a new target image, denoted as Fi , is assigned using the corresponding regional classiﬁer Fi = fr xi , 30 G. Sanroma et al. where xi denotes the target image features at position i.