A Generative Adversarial Network for AI-Aided Chair Design

We present a method for improving human design of chairs. The goal of the method is generating enormous chair candidates in order to facilitate human designer by creating sketches and 3d models accordingly based on the generated chair design. It consists of an image synthesis module, which learns the underlying distribution of training dataset, a super-resolution module, which improve quality of generated image and human involvements. Finally, we manually pick one of the generated candidates to create a real life chair for illustration.Comment: 6 pages, 5 figures, accepted at MIPR201

Deep Learning for Automated Product Design

Product development is a highly complex process that has to be individually adapted depending on the companies involved, the product to be developed and the related designers. Within this process, the approach and the know-how of the designer are very individual and can often only be described with high effort in a rule-based manner. Nevertheless, numerous routine tasks can be identified that offer enormous automation potential. Machine Learning, especially Deep Learning, has proven an immense capability to identify patterns and extract knowledge out of complex data sets. Autoencoder networks are suitable for the conversion of different 3D input data, e.g. Point Clouds, into compact latent representations and vice versa. Point Clouds are a universal representation of 3D objects and can be derived from various 3D data formats. The goal of the approach presented is to use Deep Learning algorithms to identify design patterns specific to a product family out of their underlying latent representation and use the extracted knowledge to automatically generate new latent object representations fulfilling distinct product feature specifications. A deep Autoencoder network with state-of-the-art reconstruction quality is used to encode Point Clouds into latent representations. In this approach, a conditional Generative Adversarial Network operating in latent space for generation of class-, characteristic- and dimension-conditioned objects is introduced. The model is quantitatively evaluated by a comparison of given specifications and the implemented features of generated objects. The presented findings can be used to support designers in the creation process by automatically proposing appropriate objects as well as in the adaption of future product variants to different requirements. This relieves the designer of time-consuming routine tasks and reduces the effort of knowledge-transfer between designers significantly

A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC

Mobile Artificial Intelligence-Generated Content (AIGC) technology refers to the adoption of AI algorithms deployed at mobile edge networks to automate the information creation process while fulfilling the requirements of end users. Mobile AIGC has recently attracted phenomenal attentions and can be a key enabling technology for an emerging application, called human digital twin (HDT). HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services. To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges. Moreover, we illustrate two use cases, i.e., mobile AIGC-driven HDT in customized surgery planning and personalized medication. In addition, we conduct an experimental study to prove the effectiveness of the proposed mobile AIGC-driven HDT solution, which shows a particular application in a virtual physical therapy teaching platform. Finally, we conclude this article by briefly discussing several open issues and future directions

Secure and robust machine learning for healthcare: A survey

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research

Towards AI-Assisted Disease Diagnosis: Learning Deep Feature Representations for Medical Image Analysis

Artificial Intelligence (AI) has impacted our lives in many meaningful ways. For our research, we focus on improving disease diagnosis systems by analyzing medical images using AI, specifically deep learning technologies. The recent advances in deep learning technologies are leading to enhanced performance for medical image analysis and computer-aided disease diagnosis. In this dissertation, we explore a major research area in medical image analysis - Image classification. Image classification is the process to assign an image a label from a fixed set of categories. For our research, we focus on the problem of Alzheimer\u27s Disease (AD) diagnosis from 3D structural Magnetic Resonance Imaging (sMRI) and Positron Emission Tomography (PET) brain scans. Alzheimer\u27s Disease is a severe neurological disorder. In this dissertation, we address challenges related to Alzheimer\u27s Disease diagnosis and propose several models for improved diagnosis. We focus on analyzing the 3D Structural MRI (sMRI) and Positron Emission Tomography (PET) brain scans to identify the current stage of Alzheimer\u27s Disease: Normal Controls (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). This dissertation demonstrates ways to improve the performance of a Convolutional Neural Network (CNN) for Alzheimer\u27s Disease diagnosis. Besides, we present approaches to solve the class-imbalance problem and improving classification performance with limited training data for medical image analysis. To understand the decision of the CNN, we present methods to visualize the behavior of a CNN model for disease diagnosis. As a case study, we analyzed brain PET scans of AD and CN patients to see how CNN discriminates among data samples of different classes. Additionally, this dissertation proposes a novel approach to generate synthetic medical images using Generative Adversarial Networks (GANs). Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. Our proposed model can solve such issue and generate brain MRI and PET images for three different stages of Alzheimer\u27s Disease - Normal Control (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). Our proposed approach can be generalized to create synthetic data for other medical image analysis problems and help to develop better disease diagnosis model