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

2020-04-07Code Available1· sign in to hype

Francisco Maria Calisto, Nuno Jardim Nunes, Jacinto Carlos Nascimento

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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.

Tasks

3D Medical Imaging SegmentationAutomatic Machine Learning Model SelectionBreast Cancer DetectionBreast Mass Segmentation In Whole MammogramsBreast Tumour ClassificationInterpretable Machine LearningMathematical ProofsMedical DiagnosisMedical Image RetrievalProbabilistic Deep Learning

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
BreastDICOM4DenseNet-161Average Precision0.74—Unverified

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