Omkar2118/datasets huggingface dataset:
BreastRates4
Full name: [MIMBCD-UI] UTA4: Rates Dataset

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](https://en.wikipedia.org/wiki/BI-RADS)) of clinicians while diagnosing several 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* for the measurements of severity *rates* ([BIRADS](https://en.wikipedia.org/wiki/BI-RADS)) concerning the patient diagnostic. 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 from 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-rates/wiki) in this repository. We also have several demos to see in our [YouTube Channel](https://www.youtube.com/channel/UCPz4aTIVHekHXTxHTUOLmXw), please follow us.

Introducing paper: BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
BreastClassifications4
Full name: [MIMBCD-UI] UTA4: Severity & Pathology Classifications Dataset

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 the real results severity ([BIRADS](https://en.wikipedia.org/wiki/BI-RADS)) and pathology (post-report) *classifications* provided by the Radiologist Director from the Radiology Department of [Hospital Fernando Fonseca](https://hff.min-saude.pt/) while diagnosing several patients (see [`dataset-uta4-dicom`](https://github.com/MIMBCD-UI/dataset-uta4-dicom)) 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* for the measurements of both severity ([BIRADS](https://en.wikipedia.org/wiki/BI-RADS)) and pathology *classifications* concerning the patient diagnostic. 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 from 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-rates/wiki) in this repository. We also have several demos to see in our [YouTube Channel](https://www.youtube.com/channel/UCPz4aTIVHekHXTxHTUOLmXw), please follow us.

Introducing paper: BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis