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 a limited sampling of our medical imaging DICOM files of patients resulted from our User Tests and Analysis 11 (UTA11) study. Here, we provide a dataset/ of the used medical images during the UTA11 tasks. This repository and respective dataset should be paired with the dataset-uta11-rates repository dataset. Paired to the dataset-uta11-rates repository, this dataset-uta11-dicom repository was also used with support of the dataset-uta11-findings repository. Work and results are published under the master thesis of João Fernandes. Results were analyzed and interpreted on our sa-uta11-results repository. The user tests were made in clinical institutions, where clinicians diagnose several patients for an assertiveness-based communication. For example, in these tests, we used the prototype-assertive-proactive, prototype-assertive-reactive, prototype-non-assertive-proactive, and prototype-non-assertive-reactive repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA research works. 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 public datasets in different modes. For more information about the available datasets please follow the public datasets page on the public wiki of the meta or the private wiki of the meta-private repositories. 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.
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},
articleno = {13},
numpages = {20},
keywords = {Clinical Decision Support System, Healthcare, Breast Cancer},
location = {Hamburg, Germany},
series = {CHI '23}
}
The following list is showing the required dependencies for this project to run locally:
Here are some tutorials and documentation, if needed, to feel more comfortable about using and playing around with this repository:
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.
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-dicom.gitOptionally, the module/directory can be installed into the designated Python interpreter by moving it into the site-packages directory at the respective Python directory.
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.pyOr, you can run the shell script by:
./scripts/reader.shJust keep in mind this is just a demo, so it does nothing more than loading the DICOM image located at some path using pydicom.dcmread, which returns a Dataset object. We can access the pixel data from the dataset using the pixel_array attribute, which is a NumPy array representing the image. Finally, we use matplotlib to display the image. The cmap=plt.cm.bone argument sets the colormap to grayscale, which is often used for medical images. Also, we did our best to make the demo as user-friendly as possible, so, above everything else, have fun! 😁
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.
This project exists thanks to all the people who contribute. We welcome everyone who wants to help us improve this repository. As follows, we present some suggestions.
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.
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.
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:
To publish our datasets we used a well known platform called Kaggle. To access these datasets just follow the uta4-sm-vs-mm-sheets dataset, as an example. Here, you will find all of our published datasets and any associated information, such as descriptions and download links. Last but not least, you can also follow our work at data.world, figshare.com and openml.org platforms.
Copyright © 2022 Instituto Superior Técnico
The dataset-uta11-dicom 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.
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.
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Francisco Maria Calisto [ Website | ResearchGate | GitHub | Twitter | LinkedIn ]
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João Fernandes
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Jacinto Nascimento [ ResearchGate ]
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Nuno Nunes [ ResearchGate ]
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Carlos Santiago [ ResearchGate ]
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Alfredo Ferreira
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Bruno Cardoso
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Bruno Dias
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Bruno Oliveira
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Catarina Barata
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Daniel Gonçalves
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Hugo Lencastre [ ResearchGate ]
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João Bernardo Tavares
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Luís Ribeiro Gomes
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Madalena Pedreira
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Nádia Mourão [ ResearchGate ]
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Pedro Miraldo
This work was partially supported by national funds through FCT and IST through both UID/EEA/50009/2013 and UIDB/50009/2020 projects, BL89/2017-IST-ID grant. We thank Dr. Clara Aleluia and her radiology team of HFF for valuable insights and helping using the Assistant on their daily basis. From IPO-Lisboa, we would like to thank the medical imaging teams of Dr. José Carlos Marques and Dr. José Venâncio. From IPO-Coimbra, we would like to thank the radiology department director and the all team of Dr. Idílio Gomes. Also, we would like to provide our acknowledgments to Dr. Emília Vieira and Dr. Cátia Pedro from Hospital Santa Maria. Furthermore, we want to thank all team from the radiology department of HB for participation. Last but not least, a great thanks to Dr. Cristina Ribeiro da Fonseca, who among others is giving us crucial information for the BreastScreening project.
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.
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