Mr. Qiang Tang Profile

Qiang Tang

at Loughborough Univ

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 15 March 2019 Presentation + Paper

The wearable augmented reality (AR) approach to support mammographic training: hololens for screener training

Qiang Tang, Yan Chen, Gerald Schaefer, Alastair Gale

Proceedings Volume 10954, 1095408 (2019) https://doi.org/10.1117/12.2512425

KEYWORDS: Infrared sensors, Autoregressive models, Augmented reality, Sensors, Infrared detectors, Infrared radiation, Holograms, Cameras, Image registration, Applied research

Read Abstract +

Proceedings Article | 19 March 2018 Paper

Analysis of visual search behaviour from experienced radiologists interpreting digital breast tomosynthesis (DBT) images: a pilot study

Leng Dong, Qiang Tang, Alastair Gale, Daniella Bernardi, Yan Chen

Proceedings Volume 10577, 1057707 (2018) https://doi.org/10.1117/12.2293615

KEYWORDS: Digital breast tomosynthesis, Breast, Visualization, Eye, Visual analytics, Mammography, 3D image processing, Diagnostics

Read Abstract +

Proceedings Article | 13 March 2018 Paper

Development of an augmented reality approach to mammographic training: overcoming some real world challenges

Qiang Tang, Yan Chen, Gerald Schaefer, Alastair Gale

Proceedings Volume 10576, 105762M (2018) https://doi.org/10.1117/12.2293496

KEYWORDS: Autoregressive models, Augmented reality, Calibration, Standards development, Visualization, Virtual reality, Breast cancer, Data acquisition, Databases, Breast

Read Abstract +

A dedicated workstation and its corresponding viewing software are essential requirements in breast screener training. A major challenge of developing further generic screener training technology (in particular, for mammographic interpretation training) is that high-resolution radiological images are required to be displayed on dedicated workstations whilst actual reporting of the images is generally completed on individual standard workstations. Due to commercial reasons, dedicated clinical workstations manufactured by leading international vendors tend not to have critical technical aspects divulged which would facilitate further integration of third party generic screener training technology. With standard workstations, it is noticeable that the conventional screener training depends highly on manual transcription so that traditional training methods can potentially be deficient in terms of real-time feedback and interaction. Augmented reality (AR) provides the ability to co-operate with both real and virtual environments, and therefore can supplement conventional training with virtual registered objects and actions. As a result, realistic screener training can co-operate with rich feedback and interaction in real time. Previous work¹ has shown that it is feasible to employ an AR approach to deliver workstation-independent radiological screening training by superimposing appropriate feedback coupled with the use of interaction interfaces. The previous study addressed presence issues and provided an AR recognisable stylus which allowed for drawing interaction. As a follow-up, this study extends the AR method and investigates realistic effects and the impacts of environmental illumination, application performance and transcription. A robust stylus calibration method is introduced to address environmental changes over time. Moreover, this work introduces a completed AR workflow which allows real time recording, computer analysable training data and further recoverable transcription during post-training. A quantitative evaluation results show an accuracy of more than 80% of user-drawn points being located within a pixel distance of 5.

Proceedings Article | 10 March 2017 Presentation + Paper

The implementation of an AR (augmented reality) approach to support mammographic interpretation training: an initial feasibility study

Qiang Tang, Yan Chen, Alastair Gale

Proceedings Volume 10136, 1013604 (2017) https://doi.org/10.1117/12.2255833

KEYWORDS: Augmented reality, Process modeling, Radiology, Autoregressive models, Performance modeling, Education and training, Cameras, Glasses, Image processing, Head, Medical imaging, Computer simulations, Visualization, Surgery

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years