@article{RezaeiNaeppiLippertetal.2020,
  author    = {Rezaei, Mina and N{\"a}ppi, Janne J. and Lippert, Christoph and Meinel, Christoph and Yoshida, Hiroyuki},
  title     = {Generative multi-adversarial network for striking the right balance in abdominal image segmentation},
  series = {International journal of computer assisted radiology and surgery},
  volume    = {15},
  journal   = {International journal of computer assisted radiology and surgery},
  number    = {11},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1861-6410},
  doi       = {10.1007/s11548-020-02254-4},
  pages     = {1847 -- 1858},
  year      = {2020},
  abstract  = {Purpose: The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method: The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result: In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion: The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.},
  language  = {en}
}
@article{SchroederHoehle2011,
  author    = {Schr{\"o}der, C. and H{\"o}hle, Barbara},
  title     = {Prosodic perception during early language acquisition},
  series = {Sprache, Stimme, Geh{\"o}r : Zeitschrift f{\"u}r Kommunikationsst{\"o}rungen},
  volume    = {35},
  journal   = {Sprache, Stimme, Geh{\"o}r : Zeitschrift f{\"u}r Kommunikationsst{\"o}rungen},
  number    = {3},
  publisher = {Thieme},
  address   = {Stuttgart},
  issn      = {0342-0477},
  doi       = {10.1055/s-0031-1284404},
  pages     = {E91 -- E98},
  year      = {2011},
  abstract  = {Prosody plays an important role in early language acquisition that in most children proceeds rapidly and easily. From birth on infants are able to perceive prosodic information in the speech signal. During the course of the first year of life prosodic perception abilities continue to develop. Cross-linguistic studies have shown that this development is already influenced by the native language. As prosodic and syntactic units occur often in correlation, prosodic cues in the continuous speech signal might help infants to derive information on how to segment their native language into syntactically relevant units. Indeed, infants use their prosodic perception and are able to detect word, phrase and clause boundaries using prosodic cues from the speech signal. Thus, during the first year of life when perceiving speech the processing of prosodic cues is focussed and allows for an efficient access to language acquisition. Future studies need to determine whether early prosodic perception abilities can provide markers for later language development and predict language impairment.},
  language  = {de}
}