Shoko Miyauchi

Chujie Chang, Shoko Miyauchi, Ken'ichi Morooka et al.

Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40-60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four-dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git

Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume

We propose a new neural network, called isomorphic mesh generator (iMG), which generates isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects have a unified mesh structure even though the objects belong to different classes. This unified representation enables surface models to be handled by DNNs. Moreover, the unified mesh structure of isomorphic meshes enables the same process to be applied to all isomorphic meshes; although in the case of general mesh models, we need to consider the processes depending on their mesh structures. Therefore, the use of isomorphic meshes leads to efficient memory usage and calculation time compared with general mesh models. As iMG is a data-free method, preparing any point clouds as training data in advance is unnecessary, except a point cloud of the target object used as the input data of iMG. Additionally, iMG outputs an isomorphic mesh obtained by mapping a reference mesh to a given input point cloud. To estimate the mapping function stably, we introduce a step-by-step mapping strategy. This strategy achieves a flexible deformation while maintaining the structure of the reference mesh. From simulation and experiments using a mobile phone, we confirmed that iMG can generate isomorphic meshes of given objects reliably even when the input point cloud includes noise and missing parts.

Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey et al.

This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabilities to new unseen individuals due to inter-subject variability, i.e., the same activity is performed differently by different individuals. To address this problem, we propose a novel deep adversarial framework that integrates the concept of inter-subject variability in the adversarial task, thereby encouraging subject-invariant feature representations and enhancing the classification performance in the HAR problem. Our approach outperforms previous methods in three well-established HAR datasets using a leave-one-subject-out (LOSO) cross-validation. Further results indicate that our proposed adversarial task effectively reduces inter-subject variability among different users in the feature space, and it outperforms adversarial tasks from previous works when integrated into our framework. Code: https://github.com/FranciscoCalatrava/EmbeddedSubjectVariability.git