What is it about?
In this work [1], we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening [2, 3]. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
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Photo by National Cancer Institute on Unsplash
Why is it important?
We summarize our work with recommendations from the radiologists for guiding the future design of medical imaging interfaces. Our work and contributions included: a) identifying the main clinical workflow issues, the interaction cognitive load challenges and the opportunities; b) establishing a set of design goals for medical imaging design; c) the design, reflections, and in-situ evaluation of BreastScreening supporting the clinical translation; and d) the impact evidence of Multi-Modality in diagnosing and severity classification of breast lesions with 31 radiologists in six different clinical institutions. Our results show that the system can lead to a more efficient and accurate clinical diagnosis.
Perspectives
In this work, it is proposed a new medical imaging framework supported by an interactive UI. More precisely, the development of a framework to generate a standardized dataset of medical imaging annotations. Across the domain of breast cancer, we adopt a multimodality visualization strategy (i.e., MG, US, and MRI) in order to provide clinicians a tool for the production of those qualified datasets. In the end, we foster clinicians' sharing and collaborative evaluation by developing a distributed, as well as a remotely accessible framework.
Mr. Francisco Maria Galamba Ferrari Calisto
Universidade de Lisboa
Read the Original
This page is a summary of: BreastScreening, September 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3399715.3399744.
You can read the full text:
Resources
Presentation Slides
For the purpose of this presentation, we provide the presentation slides. Also, the presentation video is included in the online resource.
Video Presentation
Remote Presentations for the International Conference on Advanced Visual Interfaces 2020.
Short Paper LaTeX Version
BreastScreening source for the LaTeX version of the manuscript.
SUS Dataset
SUS dataset resources.
NASA-TLX Dataset
NASA-TLX dataset resources.
Time Dataset
Time dataset resources.
Rates Dataset
Rates dataset with the provided BIRADS resources.
Classifications Dataset
Classifications dataset resources.
DICOM Dataset
The medical images with our used DICOM files.
Contributors
The following have contributed to this page
