First of all, this repository is highly based on the dataset-uta7-ai repository. Please reference to the documentation provide in both repositories. The present repository will be used in BreastScreening, MIDA and MIMBCD-UI projects. Similar to what we did to the dataset-uta7-classifications and dataset-uta10-classifications repositories, the dataset folder has information criteria from both mida_uta7_unanonymize and mimbcdui_uta10_patients sheets. Patients were selected from the dataset-uta7-dicom and dataset-uta10-dicom repositories. As an example, this repository is providing information of some patient data to the src/common/outputs/ folders of the prototype-assertive-proactive, prototype-assertive-reactive, prototype-non-assertive-proactive, and prototype-non-assertive-reactive repositories. 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.
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:
@article{CALISTO2021102607,
title = {Introduction of Human-Centric AI Assistant to Aid Radiologists for Multimodal Breast Image Classification},
journal = {International Journal of Human-Computer Studies},
pages = {102607},
year = {2021},
issn = {1071-5819},
doi = {https://doi.org/10.1016/j.ijhcs.2021.102607},
url = {https://www.sciencedirect.com/science/article/pii/S1071581921000252},
author = {Francisco Maria Calisto and Carlos Santiago and Nuno Nunes and Jacinto C. Nascimento},
keywords = {Human-Computer Interaction, Artificial Intelligence, Healthcare, Medical Imaging, Breast Cancer},
abstract = {In this research, we take an HCI perspective on the opportunities provided by AI techniques in medical imaging, focusing on workflow efficiency and quality, preventing errors and variability of diagnosis in Breast Cancer. Starting from a holistic understanding of the clinical context, we developed BreastScreening to support Multimodality and integrate AI techniques (using a deep neural network to support automatic and reliable classification) in the medical diagnosis workflow. This was assessed by using a significant number of clinical settings and radiologists. Here we present: i) user study findings of 45 physicians comprising nine clinical institutions; ii) list of design recommendations for visualization to support breast screening radiomics; iii) evaluation results of a proof-of-concept BreastScreening prototype for two conditions Current (without AI assistant) and AI-Assisted; and iv) evidence from the impact of a Multimodality and AI-Assisted strategy in diagnosing and severity classification of lesions. The above strategies will allow us to conclude about the behaviour of clinicians when an AI module is present in a diagnostic system. This behaviour will have a direct impact in the clinicians workflow that is thoroughly addressed herein. Our results show a high level of acceptance of AI techniques from radiologists and point to a significant reduction of cognitive workload and improvement in diagnosis execution.}
}
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-assistant-outputs.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.pyJust 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! 😁
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 downloader. 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 our project's Profile Page just follow the link.
Copyright © 2020 Instituto Superior Técnico
The dataset-assistant-outputs 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 ]
-
Hugo Lencastre [ ResearchGate ]
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Nádia Mourão [ ResearchGate ]
- Bruno Oliveira
- Luís Ribeiro Gomes
- Carlos Santiago
- Jacinto C. Nascimento
- Pedro Miraldo
- Nuno Nunes
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 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.
This project exists thanks to all the people who contribute. [Contribute].
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