Isomorphic Mesh Generation from Point Clouds with Multilayer Perceptrons.

A novel neural network called the isomorphic mesh generator (iMG) is proposed to generate isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects exhibit a unified mesh structure, despite objects belonging to different classes. This unified representation enables various modern deep neural networks (DNNs) to easily handle surface models without requiring additional pre-processing.

Personalized Federated Learning for Institutional Prediction Model using Electronic Health Records: A Covariate Adjustment Approach.

Federated learning (FL) has attracted attention as a technology that allows multiple medical institutions to collaborate on AI without disclosing each other's patient data. However, FL has the challenge of being unable to robustly learn when the data of participating clients is non-independently and non-identically distributed (Non-IID). Personalized Federated Learning (PFL), which constructs a personalized model for each client, has been proposed as a solution to this problem.

Augmented reality-based affective training for improving care communication skill and empathy.

It is important for caregivers of people with dementia (PwD) to have good patient communication skills as it has been known to reduce the behavioral and psychological symptoms of dementia (BPSD) of PwD as well as caregiver burnout. However, acquiring such skills often requires one-on-one affective training, which can be costly. In this study, we propose affective training using augmented reality (AR) for supporting the acquisition of such skills.

Artificial Intelligence for Segmentation of Bladder Tumor Cystoscopic Images Performed by U-Net with Dilated Convolution.

Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed at assessing the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net.

Speed invariant gait recognition-The enhanced mutual subspace method.

This paper introduces an enhanced MSM (Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. The enhanced MSM (eMSM) methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace.

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades.

Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies.

Background And Objective: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs.

Methods: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM.

An informationally structured room for robotic assistance.

The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people.

Estimation of brain internal structures by deforming brain atlas using finite element method.

This paper presents a method for estimating the internal structures of a patient brain by deforming a standard brain atlas. Conventional deformation methods need several landmarks from the brain surface contour to fit the atlas to the patient brain shape. However, since the number and shapes of small sulci on the brain surface are different from each other, the determination of the accurate correspondence between small sulcus is difficult for experienced neurosurgeons.

Floor sensing system using laser reflectivity for localizing everyday objects and robot.

This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall.

Navigation system with real-time finite element analysis for minimally invasive surgery.

This paper presents a navigation system for minimally invasive surgery, especially laparoscopic surgery in which operates in abdomen. Conventional navigation systems show virtual images by superimposing models of target tissues on real endoscopic images. Since soft tissues within the abdomen are deformed during the surgery, the navigation system needs to provide surgeons reliable information by deforming the models according to their biomechanical behavior.

Gait-based person identification robust to changes in appearance.

The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects' appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance.

Categorization of indoor places using the Kinect sensor.

The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion.

Real-time nonlinear FEM with neural network for simulating soft organ model deformation.

This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. that a deformed model can be estimated from the superposition of basic deformation modes.