Papers › BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

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

7 Apr 2020arXiv:2004.03500archive 2025-07-28

Francisco Maria Calisto, Nuno Jardim Nunes, Jacinto Carlos Nascimento

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.

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Code

MIMBCD-UI/prototype-multi-modality official report

MIMBCD-UI/avi-2020-short-paper mentioned on GitHub report

MIMBCD-UI/dataset-uta4-dicom report

MIMBCD-UI/dataset-uta4-nasa-tlx report

MIMBCD-UI/dataset-uta4-rates report

MIMBCD-UI/dataset-uta4-sus report

MIMBCD-UI/dataset-uta4-time report

mida-project/prototype-multi-modality-assistant report

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Tasks

3D Medical Imaging Segmentation Automatic Machine Learning Model Selection Breast Cancer Detection Breast Mass Segmentation In Whole Mammograms Breast Tumour Classification Interpretable Machine Learning Mathematical Proofs Medical Diagnosis Medical Image Retrieval Probabilistic Deep Learning

Datasets

Introduced by this paper, per the archive.

BreastClassifications4 BreastDICOM4 BreastRates4

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Medical Diagnosis	BreastDICOM4	DenseNet-161	Average Precision	0.74	#1 of 1	Archive leaderboard	report
Medical Diagnosis	BreastDICOM4	DenseNet-161	Average Recall	0.68	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Softmax

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