arXiv subjects

Satoshi Ito

Publications and source records attributed to Satoshi Ito.

4 recordsLinked to original sources

arXiv2024

Deep-learning-based Magnetic Resonance Simultaneous Multislice Imaging Using Holographic Image Decoding

Simultaneous multislice (SMS) imaging is a one of the acceleration technique of magnetic resonance imaging. SMS requires accurate sensitivity distributions in the slice plane for each receiving coil. This requirement is difficult to satisfy in practice, limiting the applications of this imaging technique. Here, images are reconstructed by applying deep learning and amplitude modulation to each slice image. Simulation experiments show that image reconstruction can be achieved for both real- and complex-valued images. Image quality tends to decrease with increasing number of simultaneously acquired images. It is also shown that a larger difference in the phase modulation coefficients between slices tends to increase the quality of the reconstructed images. Simulation experiments and initial MR imaging experiments show promising results for this method.

physics.med-ph↗

arXiv2023

Chance-constrained probability measure optimization

Stochastic optimization with chance constraints often relies on deterministic decision-making, where optimal decisions are fixed and may be applied to a system repeatedly. A critical question arises: Can probabilistic decision-making, where probability measures/distributions are considered as decision variables, outperform deterministic decision-making in terms of the expected performance when chance constraints are present? This paper addresses this question by introducing the \textit{Chance-Constrained Probability Measure Optimization} (CCPMO) framework, which formulates the problem of optimizing probabilistic decisions under chance constraints. We first establish the existence of the optimal solution to CCPMO. Crucially, we prove that the optimal probabilistic decisions can always be represented by a probability measure concentrated on only two points, thereby reducing the CCPMO problem to an equivalent, simplified form. To solve this reduced problem, we propose a sample-based smooth approximation method. This approach leverages samples of model uncertainties to construct an approximate problem with uniform convergence and probabilistic feasibility guarantees. The approximate problem can be efficiently solved using standard nonlinear programming techniques. Finally, we validate the proposed framework through a numerical example of a quadrotor control problem under turbulent conditions. The results demonstrate that probabilistic decision-making outperforms deterministic approaches in expected performance while satisfying safety-critical chance constraints.

math.OC↗

arXiv2022

Approximate Methods for Solving Chance Constrained Linear Programs in Probability Measure Space

A risk-aware decision-making problem can be formulated as a chance-constrained linear program in probability measure space. Chance-constrained linear program in probability measure space is intractable, and no numerical method exists to solve this problem. This paper presents numerical methods to solve chance-constrained linear programs in probability measure space for the first time. We propose two solvable optimization problems as approximate problems of the original problem. We prove the uniform convergence of each approximate problem. Moreover, numerical experiments have been implemented to validate the proposed methods.

math.OC↗

arXiv2017

BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks

We present a simple and effective framework for simultaneous semantic segmentation and instance segmentation with Fully Convolutional Networks (FCNs). The method, called BiSeg, predicts instance segmentation as a posterior in Bayesian inference, where semantic segmentation is used as a prior. We extend the idea of position-sensitive score maps used in recent methods to a fusion of multiple score maps at different scales and partition modes, and adopt it as a robust likelihood for instance segmentation inference. As both Bayesian inference and map fusion are performed per pixel, BiSeg is a fully convolutional end-to-end solution that inherits all the advantages of FCNs. We demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.

cs.CV↗