Appendix

For the MIMBCD-UI Project we have several Appendix information. The information is concentrated here in this Wiki it serves the purpose of bringing together the sets of information. These sets of information are information that is not essential to explain, at least here, for our findings.

Index

• Appendix

• Channels

• Datasets

• Essentials

• FAQ

• Presentations

• Prototypes

• Publications

• Technical

• Tools

• User Research

Single-Modality vs Multi-Modality

• Vertical (View)

• Horizontal (View)

• Window Manager

• Toolbar Single

• Toolbar Multi

• Toolbars (View)

• Patient List Base

• Patient List Labels (View)

• UI MRI

• UI MRI & US

• Radiology Room (View)

• Both Views Animation

• Participatory and Personalized Diagnostic Systems Diagram (View)

• Participatory and Personalized Diagnostic Systems (View)

• UTA4: Datasets Sheet (View)

Radiology Room Workflow

• Image Source (View)

Deployment Environments

• Image Source (View)

BreastScreening Workflow

• Image Source (View)

Tools Extended

• Image Source

Giphy

• GIF Source

Assistant

• BreastScreening Assistant Architecture (v1.2.0)

• UTA7: DenseNet-161 Feature Extration Dropout Flowchart

• Medical Imaging Downloader (MID): Setup Flow Chart Orthanc and CornerstoneJS

• Medical Imaging Downloader (MID): Setup Flow Chart Server and Viewer

• BreastScreening Segmentation Comparisons: Option 1 (Bounding Box)

• BreastScreening Segmentation Comparisons: Option 2 (Lesion Countours)

• BreastScreening Segmentation Comparisons: Option 3 (Heatmaps)

• BreastScreening Segmentation Comparisons: Option 4 (Binary Images)

• Radiomics Automatic Lesion Segmentation (View)

• THCDIAMID: Thesis Proposal Resumed Problems and Contributions (View)

Invention Proposals

• Annoptation System Toolbar (View)

• Workshops & Focus Groups Basic Numbers (View)

• MIMBCD-UI: System Components Schematic Demonstration (View)

• MIMBCD-UI: Clinicians Annotating JSON Files Flow Diagram (View)

Posts

• Accelerating AI Adoption with Explainability in SaMD

• Introducing AI Explainability 360