{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","arxiv_id":"2004.03500","date":"2020-04-07","proceeding":null,"authors":["Francisco Maria Calisto","Nuno Jardim Nunes","Jacinto Carlos Nascimento"],"abstract":"This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of single vs multimodality screening of breast cancer diagnosis with 31 clinicians and 566 images, and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI). We summarize our work with recommendations from the radiologists for guiding the future design of medical imaging interfaces.","url_abs":"https://arxiv.org/abs/2004.03500v2","url_pdf":"https://arxiv.org/pdf/2004.03500v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/prototype-multi-modality","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/avi-2020-short-paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/dataset-uta4-dicom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/dataset-uta4-nasa-tlx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/dataset-uta4-rates","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/dataset-uta4-sus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/MIMBCD-UI/dataset-uta4-time","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"breastscreening-on-the-use-of-multi-modality","repo_url":"https://github.com/mida-project/prototype-multi-modality-assistant","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"automatic-machine-learning-model-selection","task_name":"Automatic Machine Learning Model Selection"},{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"breast-mass-segmentation-in-whole-mammograms","task_name":"Breast Mass Segmentation In Whole Mammograms"},{"task_slug":"breast-tumour-classification","task_name":"Breast Tumour Classification"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"},{"task_slug":"mathematical-proofs","task_name":"Mathematical Proofs"},{"task_slug":"medical-diagnosis","task_name":"Medical Diagnosis"},{"task_slug":"medical-image-retrieval","task_name":"Medical Image Retrieval"},{"task_slug":"probabilistic-deep-learning","task_name":"Probabilistic Deep Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"breastclassifications4","name":"BreastClassifications4","full_name":"[MIMBCD-UI] UTA4: Severity & Pathology Classifications Dataset"},{"slug":"mimbcd-ui-uta7-medical-imaging-dicom-files","name":"BreastDICOM4","full_name":"[MIMBCD-UI] UTA4: Medical Imaging DICOM Files Dataset"},{"slug":"breastrates4","name":"BreastRates4","full_name":"[MIMBCD-UI] UTA4: Rates Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-diagnosis-on-mimbcd-ui-uta7-medical","task":"Medical Diagnosis","dataset":"BreastDICOM4","model":"DenseNet-161","rank_in_archive_order":1,"of":1,"metrics":{"Average Precision":"0.74","Average Recall":"0.68"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}