Pseudo-data generation apparatus, pseudo-data generation method, learning apparatus and learning method
According to one embodiment, a pseudo-data generation apparatus includes processing circuitry. The processing circuitry acquires one or more pieces of partial observation data that form part of whole observation data. The processing circuitry generates pseudo-whole observation data by inputting the one or more pieces of partial observation data to a function, the pseudo-whole observation data being pseudo-data of the whole observation data. The function is optimized by training so that partial observation data for training and pseudo-partial observation data for training resemble each other, the pseudo-partial observation data for training being obtained by converting the pseudo-whole observation data for training.
1 . A pseudo-data generation apparatus for generating whole observation data which is unavailable and impossible to observe, the pseudo-data generation method comprising:
processing circuitry configured to:
acquire one or more pieces of partial observation data that form part of the whole observation data, the whole observation data being three-dimensional volume magnetic resonance moving image data; and
generate pseudo-whole observation data by inputting the one or more pieces of partial observation data to a function, the pseudo-whole observation data being pseudo-data of the whole observation data,
wherein the function is optimized by training, without using the unavailable whole observation data, so that partial observation data for training and pseudo-partial observation data for training resemble each other, the pseudo-partial observation data for training being obtained by converting pseudo-whole observation data for training, and
wherein the pseudo-whole observation data for training is generated using the function, from the partial observation data for training.
2 . The pseudo-data generation apparatus according to claim 1 , wherein the function is a generator trained using a conditional generative adversarial network, a decoder trained using a conditional variational auto encoder, or a model trained using a conditional diffusion model.
3 . The pseudo-data generation apparatus according to claim 1 , wherein the processing circuitry is further configured to:
acquire, as multiple pieces of the partial observation data, a plurality of images captured while shifting a focus, and
generate three-dimensional volume data as the pseudo-whole observation data from the multiple pieces of the partial observation data, the three-dimensional volume data including depth information.
4 . A pseudo-data generation method for generating whole observation data which is impossible to observe and unavailable, the pseudo-data generation method comprising:
acquiring one or more pieces of partial observation data that form part of whole observation data, the whole observation data being three-dimensional volume magnetic resonance moving image data; and
generating pseudo-whole observation data by inputting the one or more pieces of partial observation data to a function, the pseudo-whole observation data being pseudo-data of the whole observation data,
wherein the function is optimized by training, without using the unavailable whole observation data, so that partial observation data for training and pseudo-partial observation data for training resemble each other, the pseudo-partial observation data for training being obtained by converting pseudo-whole observation data for training, and
wherein the pseudo-whole observation data for training is generated using the function, from the partial observation data for training.