Abstract: Provided is a model parameter learning device and the like capable of learning model parameters such that the influence of a noise in input data can be suppressed. A model parameter learning device (1) alternately carries out first learning processing for learning model parameters W1, b1, W2 and b2 such that an error between data Xout and data Xorg is minimized, and second learning processing for learning model parameters W1, b1, Wm, bm, Wq and bq such that a loss function LAE is minimized.

Abstract: Provided is a model parameter learning device and the like capable of learning model parameters such that the influence of a noise in input data can be suppressed. A model parameter learning device (1) alternately carries out first learning processing for learning model parameters W1, b1, W2 and b2 such that an error between data Xout and data Xorg is minimized, and second learning processing for learning model parameters W1, b1, Wm, bm, Wq and bq such that a loss function LAE is minimized.

Abstract: This invention provides an X-ray CT image processing method that allows more flexible expression by expressing X-ray absorption coefficients probabilistically, makes it possible to acquire reconstructed images that are comparable to those obtained by conventional methods but involve lower X-ray doses, and can reduce beam-hardening artifacts. A probability distribution for the observation of projected X-rays is set and statistical inference is performed. Said probability distribution is expressed in terms of the process of observing a multiple-X-ray sum resulting from multiple projected X-rays being incident upon a detector. Bayesian inference in which the expected value of the posterior distribution is used for statistical inference is performed on the basis of a prior distribution for X-ray absorption coefficients, said prior distribution having parameters for the material and the observation process in terms of which the multiple-X-ray sum is expressed.

Abstract: This invention provides an X-ray CT image processing method that allows more flexible expression by expressing X-ray absorption coefficients probabilistically, makes it possible to acquire reconstructed images that are comparable to those obtained by conventional methods but involve lower X-ray doses, and can reduce beam-hardening artifacts. A probability distribution for the observation of projected X-rays is set and statistical inference is performed. Said probability distribution is expressed in terms of the process of observing a multiple-X-ray sum resulting from multiple projected X-rays being incident upon a detector. Bayesian inference in which the expected value of the posterior distribution is used for statistical inference is performed on the basis of a prior distribution for X-ray absorption coefficients, said prior distribution having parameters for the material and the observation process in terms of which the multiple-X-ray sum is expressed.

Abstract: An x-ray CT image processing method which carries out a statistical estimate in prior knowledge relating to the movement and the x-ray absorption coefficient of a measurement subject comprises: a step of hypothesizing that the measurement subject changes smoothly over time, and defining a first probability model (prior knowledge of the measurement subject over all times) relating to movement and a second probability model (projected image observation model over all times) relating to observation; and a step of carrying out a statistical estimate dependent on both the first probability model and the second probability model. The first probability model relating to the movement of the measurement subject and the second model relating to observation are defined as probability models with which statistical estimates are carried out at the outset, and motion and CT images are simultaneously estimated by carrying out induction based on the probability models.

Abstract: An x-ray CT image processing method which carries out a statistical estimate in prior knowledge relating to the movement and the x-ray absorption coefficient of a measurement subject comprises: a step of hypothesizing that the measurement subject changes smoothly over time, and defining a first probability model (prior knowledge of the measurement subject over all times) relating to movement and a second probability model (projected image observation model over all times) relating to observation; and a step of carrying out a statistical estimate dependent on both the first probability model and the second probability model. The first probability model relating to the movement of the measurement subject and the second model relating to observation are defined as probability models with which statistical estimates are carried out at the outset, and motion and CT images are simultaneously estimated by carrying out induction based on the probability models.

Abstract: A face model providing portion provides an stored average face model to an estimation portion estimating an affine parameter for obtaining a head pose. An individual face model learning portion obtains a result of tracking feature points by the estimation portion and learns an individual face model. The individual face model learning portion terminates the learning when a free energy of the individual face model is over a free energy of the average face model, and switches a face model provided to the estimation portion from the average face model to the individual face model. While learning the individual face mode, an observation matrix is factorized using a reliability matrix showing reliability of each observation value forming the observation matrix with emphasis on the feature point having higher reliability.

Abstract: An estimation device estimates a hidden state of an estimation subject from an observable state in a manner of a time series. The observable state is observed from the hidden state of the estimation subject under a procedure that has a hierarchical structure, which includes the hidden state of the estimation subject, the observable state, and an intermediate hidden state therebetween. The estimation device includes an estimation subject hidden state predicting means, an intermediate hidden state predicting means based on the state transition structure of the hidden state of the estimation subject, an intermediate hidden state likelihood observing means, an intermediate hidden state estimating means, an estimation subject hidden state likelihood observing means, estimation subject hidden state estimating means, an intermediate hidden state predicting means based on the state transition structure of the intermediate hidden state, and the mixing means.

Abstract: A microarray for predicting the prognosis of neuroblastoma, wherein the microarray has 25 to 45 probes related to good prognosis, which are hybridized to a gene transcript whose expression is increased in a good prognosis patient with neuroblastoma and are selected from 96 polynucleotides consisting of the nucleotide sequences of SEQ. ID NOs. 1, 5, 6, 14. 16, 17, 19, 22-24, 28, 29, 31, 37, 39, 40, 43, 44, 47-52, 54, 57-60, 62, 64, 65, 67, 68, 72-75, 77, 78, 80-82, 84, 87, 89-91, 94, 100, 103, 112, 113, 118, 120, 129, 130, 132, 136, 138, 142, 144, 145, 148, 150-153, 155, 158-160, 163-165, 169-171, 173, 174, 177, 178, 180-182, 184, 186, 187, 189, 191, 192, 194, 195, 198-200 or their partial continuous sequences or their complementary strands, and 25 to 45 probes related to poor prognosis, which are hybridized to a gene transcript whose expression is increased in a poor prognosis patient with neuroblastoma and are selected from 104 polynucleotides consisting of the nucleotide sequences of SEQ. ID NOs.

Abstract: A face model providing portion provides an stored average face model to an estimation portion estimating an affine parameter for obtaining a head pose. An individual face model learning portion obtains a result of tracking feature points by the estimation portion and learns an individual face model. The individual face model learning portion terminates the learning when a free energy of the individual face model is over a free energy of the average face model, and switches a face model provided to the estimation portion from the average face model to the individual face model. While learning the individual face mode, an observation matrix is factorized using a reliability matrix showing reliability of each observation value forming the observation matrix with emphasis on the feature point having higher reliability.

Abstract: An estimation device estimates a hidden state of an estimation subject from an observable state in a manner of a time series. The observable state is observed from the hidden state of the estimation subject under a procedure that has a hierarchical structure, which includes the hidden state of the estimation subject, the observable state, and an intermediate hidden state therebetween. The estimation device includes an estimation subject hidden state predicting means, an intermediate hidden state predicting means based on the state transition structure of the hidden state of the estimation subject, an intermediate hidden state likelihood observing means, an intermediate hidden state estimating means, an estimation subject hidden state likelihood observing means, estimation subject hidden state estimating means, an intermediate hidden state predicting means based on the state transition structure of the intermediate hidden state, and the mixing means.

Abstract: A microarray for predicting the prognosis of neuroblastoma, wherein the microarray has 25 to 45 probes related to good prognosis, which are hybridized to a gene transcript whose expression is increased in a good prognosis patient with neuroblastoma and are selected from 96 polynucleotides consisting of the nucleotide sequences of Seq. ID No. 1 to 96 or their partial continuous sequences or their complementary strands, and 25 to 45 probes related to poor prognosis, which are hybridized to a gene transcript whose expression is increased in a poor prognosis patient with neuroblastoma and are selected from 104 polynucleotides consisting of the nucleotide sequences of Seq. ID No. 97 to 200 or their partial continuous sequences or their complementary strands.