Multi-modality relation attention network for breast tumor classification.

Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images.

Feature enhancement network for CNV typing in optical coherence tomography images.

. Choroidal neovascularization (CNV) is a characteristic feature of wet age-related macular degeneration, which is one of the main causes of blindness in the elderly. Automatic classification of CNV in optical coherence tomography images plays an auxiliary role in the clinical treatment of CNV.