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Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the `a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The...
We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic...
Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the `a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The...
Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation...
Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation...
We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic...
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