arXiv subjects

Xiang 'Anthony' Chen

Publications and source records attributed to Xiang 'Anthony' Chen.

42 records · Page 3Linked to original sources

arXiv2020

OralViewer: 3D Demonstration of Dental Surgeries for Patient Education with Oral Cavity Reconstruction from a 2D Panoramic X-ray

Patient's understanding on forthcoming dental surgeries is required by patient-centered care and helps reduce fear and anxiety. Due to the gap of expertise between patients and dentists, conventional techniques of patient education are usually not effective for explaining surgical steps. In this paper, we present \textit{OralViewer} -- the first interactive application that enables dentist's demonstration of dental surgeries in 3D to promote patients' understanding. \textit{OralViewer} takes a single 2D panoramic dental X-ray to reconstruct patient-specific 3D teeth structures, which are then assembled with registered gum and jaw bone models for complete oral cavity modeling. During the demonstration, \textit{OralViewer} enables dentists to show surgery steps with virtual dental instruments that can animate effects on a 3D model in real-time. A technical evaluation shows our deep learning based model achieves a mean Intersection over Union (IoU) of 0.771 for 3D teeth reconstruction. A patient study with 12 participants shows \textit{OralViewer} can improve patients' understanding of surgeries. An expert study with 3 board-certified dentists further verifies the clinical validity of our system.

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arXiv2020

FaceOff: Detecting Face Touching with a Wrist-Worn Accelerometer

According to the CDC, one key step of preventing oneself from contracting coronavirus (COVID-19) is to avoid touching eyes, nose, and mouth with unwashed hands. However, touching one's face is a frequent and spontaneous behavior---one study observed subjects touching their faces on average 23 times per hour. Creative solutions have emerged amongst some recent commercial and hobbyists' projects, yet most either are closed-source or lack validation in performance. We develop FaceOff---a sensing technique using a commodity wrist-worn accelerometer to detect face-touching behavior based on the specific motion pattern of raising one's hand towards the face. We report a survey (N=20) that elicits different ways people touch their faces, an algorithm that temporally ensembles data-driven models to recognize when a face touching behavior occurs and results from a preliminary user testing (N=3 for a total of about 90 minutes).

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arXiv2020

Romeo: A Design Tool for Embedding Transformable Parts in 3D Models to Robotically Augment Default Functionalities

Reconfiguring shapes of objects enables transforming existing passive objects with robotic functionalities, e.g., a transformable coffee cup holder can be attached to a chair's armrest, a piggy bank can reach out an arm to 'steal' coins. Despite the advance in end-user 3D design and fabrication, it remains challenging for non-experts to create such 'transformables' using existing tools due to the requirement of specific engineering knowledge such as mechanisms and robotic design. We present Romeo -- a design tool for creating transformables to robotically augment objects' default functionalities. Romeo allows users to transform an object into a robotic arm by expressing at a high level what type of task is expected. Users can select which part of the object to be transformed, specify motion points in space for the transformed part to follow and the corresponding action to be taken. Romeo then automatically generates a robotic arm embedded in the transformable part ready for fabrication. A design session validated this tool where participants used Romeo to accomplish controlled design tasks and to open-endedly create coin-stealing piggy banks by transforming 3D objects of their own choice.

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arXiv2020

Geno: A Developer Tool for Authoring Multimodal Interaction on Existing Web Applications

Supporting voice commands in applications presents significant benefits to users. However, adding such support to existing GUI-based web apps is effort-consuming with a high learning barrier, as shown in our formative study, due to the lack of unified support for creating multimodal interfaces. We present Geno---a developer tool for adding the voice input modality to existing web apps without requiring significant NLP expertise. Geno provides a high-level workflow for developers to specify functionalities to be supported by voice (intents), create language models for detecting intents and the relevant information (parameters) from user utterances, and fulfill the intents by either programmatically invoking the corresponding functions or replaying GUI actions on the web app. Geno further supports multimodal references to GUI context in voice commands (e.g. "move this [event] to next week" while pointing at an event with the cursor). In a study, developers with little NLP expertise were able to add multimodal voice command support for two existing web apps using Geno.

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arXiv2020

CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis

The recent development of data-driven AI promises to automate medical diagnosis; however, most AI functions as 'black boxes' to physicians with limited computational knowledge. Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain---a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: (1) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (2) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (3) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI. We summarize by discussing recommendations for future work to design and implement explainable medical AI systems that encompass four recurring themes: motivation, constraint, explanation, and justification.

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arXiv2019

Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis

The adoption of intelligent systems creates opportunities as well as challenges for medical work. On the positive side, intelligent systems have the potential to compute complex data from patients and generate automated diagnosis recommendations for doctors. However, medical professionals often perceive such systems as black boxes and, therefore, feel concerned about relying on system generated results to make decisions. In this paper, we contribute to the ongoing discussion of explainable artificial intelligence (XAI) by exploring the concept of explanation from a human-centered perspective. We hypothesize that medical professionals would perceive a system as explainable if the system was designed to think and act like doctors. We report a preliminary interview study that collected six medical professionals' reflection of how they interact with data for diagnosis and treatment purposes. Our data reveals when and how doctors prioritize among various types of data as a central part of their diagnosis process. Based on these findings, we outline future directions regarding the design of XAI systems in the medical context.

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