{"url":"/dataset/mimbcd-ui-uta7-medical-imaging-dicom-files","name":"BreastDICOM4","full_name":"[MIMBCD-UI] UTA4: Medical Imaging DICOM Files Dataset","description_markdown":"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 medical imaging [DICOM](https://en.wikipedia.org/wiki/DICOM) files of patients from our [User Tests and Analysis 4 (UTA4)](https://github.com/MIMBCD-UI/meta/wiki/User-Research#test-4-single-modality-vs-multi-modality-) study. Here, we provide a *dataset* of the used medical images during the [UTA4](https://github.com/MIMBCD-UI/meta/wiki/User-Research#test-4-single-modality-vs-multi-modality-) tasks. This repository and respective *dataset* should be paired with the [`dataset-uta4-rates`](https://github.com/MIMBCD-UI/dataset-uta4-rates) repository *dataset*. Work and results are published on a top [Human-Computer Interaction (HCI)](https://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction) conference named [AVI 2020](https://dl.acm.org/conference/avi) ([page](https://sites.google.com/unisa.it/avi2020)). Results were analyzed and interpreted on our [Statistical Analysis](https://mimbcd-ui.github.io/statistical-analysis/) charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a **Single-Modality** *vs* **Multi-Modality** comparison. For example, in these tests, we used both [`prototype-single-modality`](https://github.com/mida-project/prototype-single-modality) and [`prototype-multi-modality`](https://github.com/mida-project/prototype-multi-modality) repositories for the comparison. On the same hand, the hereby *dataset* represents the pieces of information of both [BreastScreening](https://BreastScreening.github.io) and [MIDA](https://mida-project.github.io) projects. These projects are research projects that deal with the use of a recently proposed technique in literature: [Deep Convolutional Neural Networks (CNNs)](https://en.wikipedia.org/wiki/Convolutional_neural_network). From a developed User Interface (UI) and *framework*, these deep networks will incorporate [several datasets](https://github.com/MIMBCD-UI/meta/wiki/Datasets) in different modes. For more information about the available *datasets* please follow the [Datasets](https://github.com/MIMBCD-UI/meta/wiki/Datasets) page on the [Wiki](https://github.com/MIMBCD-UI/meta/wiki) of the [`meta`](https://github.com/MIMBCD-UI/meta) information repository. Last but not least, you can find further information on the [Wiki](https://github.com/MIMBCD-UI/dataset-uta4-dicom/wiki) in this repository. We also have several demos to see in our [YouTube Channel](https://www.youtube.com/channel/UCPz4aTIVHekHXTxHTUOLmXw), please follow us.","description_withheld":null,"homepage":"https://mimbcd-ui.github.io/dataset-uta4-dicom","introduced_date":"2020-10-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","first_author":"Francisco Maria Calisto","url":null},"license":{"name":"AGPL-3.0","url":"https://github.com/MIMBCD-UI/dataset-uta4-dicom/blob/master/LICENSE"},"modalities":[{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Medical Image Registration","url":"/task/medical-image-registration","datasets_with_task":"/datasets/task/medical-image-registration"},{"name":"Medical Diagnosis","url":"/task/medical-diagnosis","datasets_with_task":"/datasets/task/medical-diagnosis"},{"name":"Medical Image Retrieval","url":"/task/medical-image-retrieval","datasets_with_task":"/datasets/task/medical-image-retrieval"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BreastDICOM4"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-diagnosis-on-mimbcd-ui-uta7-medical","task":"Medical Diagnosis","dataset_variant":"BreastDICOM4","rows":1,"metrics":["Average Precision","Average Recall"],"first_row_in_archive_order":{"model":"DenseNet-161","paper":"/paper/breastscreening-on-the-use-of-multi-modality","metrics":{"Average Precision":"0.74","Average Recall":"0.68"},"code_links":[{"title":"MIMBCD-UI/dataset-uta4-dicom","url":"https://github.com/MIMBCD-UI/dataset-uta4-dicom"},{"title":"MIMBCD-UI/prototype-multi-modality","url":"https://github.com/MIMBCD-UI/prototype-multi-modality"},{"title":"MIMBCD-UI/avi-2020-short-paper","url":"https://github.com/MIMBCD-UI/avi-2020-short-paper"},{"title":"MIMBCD-UI/dataset-uta4-rates","url":"https://github.com/MIMBCD-UI/dataset-uta4-rates"},{"title":"MIMBCD-UI/dataset-uta4-nasa-tlx","url":"https://github.com/MIMBCD-UI/dataset-uta4-nasa-tlx"},{"title":"MIMBCD-UI/dataset-uta4-sus","url":"https://github.com/MIMBCD-UI/dataset-uta4-sus"},{"title":"MIMBCD-UI/dataset-uta4-time","url":"https://github.com/MIMBCD-UI/dataset-uta4-time"},{"title":"mida-project/prototype-multi-modality-assistant","url":"https://github.com/mida-project/prototype-multi-modality-assistant"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","date":"2020-04-07","rows_on_this_dataset":1,"code_links":8,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}