This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
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
The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.
The learned features and the classification results are used to retrieve medical images.
This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types.