UTA11: Rates Dataset

Welcome to this dataset repository for our paper “Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis” (10.1145/3544548.3580682) in proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ‘23) presented during the “AI in Health” track. In our work, several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our severity rates (BIRADS) of clinicians while diagnosing several patients from our User Tests and Analysis 11 (UTA11) study. Here, we provide a dataset for the measurements of severity rates (BIRADS) concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named CHI 2023 (page). Results were analyzed and interpreted from our sa-uta11-results repository charts. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

Citing

We kindly ask scientific works and studies that make use of the repository to cite it in their associated publications. Similarly, we ask open-source and closed-source works that make use of the repository to warn us about this use.

You can cite our work using the following BibTeX entry:

@inproceedings{10.1145/3544548.3580682,
author = {Calisto, Francisco Maria and Fernandes, Jo\~{a}o and Morais, Margarida and Santiago, Carlos and Abrantes, Jo\~{a}o Maria and Nunes, Nuno and Nascimento, Jacinto C.},
title = {Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis},
year = {2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3580682},
doi = {10.1145/3544548.3580682},
abstract = {Intelligent agents are showing increasing promise for clinical decision-making in a variety of healthcare settings. While a substantial body of work has contributed to the best strategies to convey these agents’ decisions to clinicians, few have considered the impact of personalizing and customizing these communications on the clinicians’ performance and receptiveness. This raises the question of how intelligent agents should adapt their tone in accordance with their target audience. We designed two approaches to communicate the decisions of an intelligent agent for breast cancer diagnosis with different tones: a suggestive (non-assertive) tone and an imposing (assertive) one. We used an intelligent agent to inform about: (1) number of detected findings; (2) cancer severity on each breast and per medical imaging modality; (3) visual scale representing severity estimates; (4) the sensitivity and specificity of the agent; and (5) clinical arguments of the patient, such as pathological co-variables. Our results demonstrate that assertiveness plays an important role in how this communication is perceived and its benefits. We show that personalizing assertiveness according to the professional experience of each clinician can reduce medical errors and increase satisfaction, bringing a novel perspective to the design of adaptive communication between intelligent agents and clinicians.},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
numpages = {20},
keywords = {Clinical Decision Support System, Healthcare, Breast Cancer},
location = {Hamburg, Germany},
series = {CHI '23}
}

Prerequisites
Usage
Roadmap
Contributing
License & Copyright
Team
Acknowledgements

Prerequisites

The following list is showing the required dependencies for this project to run locally:

Git or any other Git or GitHub version control tool
Python (3.5 or newer)

Here are some tutorials and documentation, if needed, to feel more comfortable about using and playing around with this repository:

Usage

Usage follow the instructions here to setup the current repository and extract the present data. To understand how the hereby repository is used for, read the following steps.

Installation

At this point, the only way to install this repository is manual. Eventually, this will be accessible through pip or any other package manager, as mentioned on the roadmap.

Nonetheless, this kind of installation is as simple as cloning this repository. Virtually all Git and GitHub version control tools are capable of doing that. Through the console, we can use the command below, but other ways are also fine.

git clone https://github.com/MIMBCD-UI/dataset-uta11-rates.git

Optionally, the module/directory can be installed into the designated Python interpreter by moving it into the site-packages directory at the respective Python directory.

Demonstration

Please, feel free to try out our demo. It is a script called demo.py at the src/ directory. It can be used as follows:

python src/demo.py

Just keep in mind this is just a demo, so it does nothing more than downloading data to an arbitrary destination directory if the directory does not exist or does not have any content. Also, we did our best to make the demo as user-friendly as possible, so, above everything else, have fun! 😁

Roadmap

We need to follow the repository goal, by addressing the thereby information. Therefore, it is of chief importance to scale this solution supported by the repository. The repository solution follows the best practices, achieving the Core Infrastructure Initiative (CII) specifications.

Besides that, one of our goals involves creating a configuration file to automatically test and publish our code to pip or any other package manager. It will be most likely prepared for the GitHub Actions. Other goals may be written here in the future.

Contributing

This project exists thanks to all the people who contribute. We welcome everyone who wants to help us improve this downloader. As follows, we present some suggestions.

Issuer

Either as something that seems missing or any need for support, just open a new issue. Regardless of being a simple request or a fully-structured feature, we will do our best to understand them and, eventually, solve them.

Developer

We like to develop, but we also like collaboration. You could ask us to add some features… Or you could want to do it yourself and fork this repository. Maybe even do some side-project of your own. If the latter ones, please let us share some insights about what we currently have.

Information

The current information will summarize important items of this repository. In this section, we address all fundamental items that were crucial to the current information.

The following list, represents the set of related repositories for the presented one:

Dataset Resources

To publish our datasets we used a well known platform called Kaggle. For the purpose, three main resources uta4-singlemodality-vs-multimodality-nasatlx, uta4-sm-vs-mm-sheets and uta4-sm-vs-mm-sheets-nameless are published in this platform. Last but not least, datasets are also published at figshare and OpenML platforms.

License & Copyright

The dataset-uta11-rates repository is distributed under the terms of GNU AGPLv3 license and CC-BY-SA-4.0 copyright. Permissions of this license are conditioned on making available complete elements from this repository of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved.

Team

Our team brings everything together sharing ideas and the same purpose, developing even better work. In this section, we will nominate the full list of important people for this repository, as well as respective links.

Authors

Francisco Maria Calisto [ Academic Website ResearchGate GitHub Twitter LinkedIn ]
João Fernandes [ ResearchGate ]
Margarida Morais [ ResearchGate ]
Carlos Santiago [ ResearchGate ]
João Maria Abrantes [ ResearchGate ]
Nuno Nunes [ ResearchGate ]
Jacinto C. Nascimento [ ResearchGate ]

Promoters

Hugo Lencastre
Nádia Mourão
Miguel Bastos
Pedro Diogo
João Bernardo
Madalena Pedreira
Mauro Machado

Companions

Bruno Dias
Bruno Oliveira
Luís Ribeiro Gomes
Pedro Miraldo

Acknowledgements

This work was partially supported by national funds through FCT and IST through the UID/EEA/50009/2013 project, BL89/2017-IST-ID grant. We thank Dr. Clara Aleluia and her radiology team of HFF for valuable insights and helping using the assistants on their daily basis. Further acknowledgments are provided inside the ACKNOWLEDGMENTS.md file of the sa-uta11-results repository. Additionally, we are grateful for the invaluable assistance provided by our colleagues of the HCII @ CMU. We are indebted to those who gave their time and expertise to evaluate our work, who among others are giving us crucial information for the BreastScreening project.

Supporting

Our organization is a non-profit organization. However, we have many needs across our activity. From infrastructure to service needs, we need some time and contribution, as well as help, to support our team and projects.