Psoriasis

Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support.

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonist...

An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications.

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presente...

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.

Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. Whi...

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care.

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capt...

Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks.

This article presents a new method for understanding and visualizing convolutional neural networks (CNNs). Most existing approaches to this problem fo...

Robust Inverse Q-Learning for Continuous-Time Linear Systems in Adversarial Environments.

This article proposes robust inverse Q -learning algorithms for a learner to mimic an expert's states and control inputs in the imitation learning pro...

A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots.

Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, c...

Harnessing interpretable machine learning for holistic inverse design of origami.

This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We establish...

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrat...

Inverse design of core-shell particles with discrete material classes using neural networks.

The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this pap...

Invertible Neural BRDF for Object Inverse Rendering.

We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and ...

A novel application of neural networks to identify potentially effective combinations of biologic factors for enhancement of bone fusion/repair.

INTRODUCTION: The use of biologic adjuvants (orthobiologics) is becoming commonplace in orthopaedic surgery. Among other applications, biologics are o...

Patient-Specific Exercises with the Development of an End-Effector Type Upper Limb Rehabilitation Robot.

End-effector type upper limb rehabilitation robots (ULRRs) are connected to patients at one distal point, making them have simple structures and less ...

Inverse free reduced universum twin support vector machine for imbalanced data classification.

Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other c...

Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot.

This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of ...

Measurement of psoriasis-affected area with artificial neural network.

Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in mu...

The three horizons model applied to medical science.

The three horizons model is a framework that helps manage an organization's innovation strategy. This model considers three aspects (horizons) that sh...

Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets.

Deep learning biomechanical models perform optimally when trained with large datasets, however these can be challenging to collect in gait labs, while...

Efficient enumeration-selection computational strategy for adaptive chemistry.

Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dy...