Sep 7, 2022 · This paper proposes a model called region-based graph convolution and the atrous spatial pyramid pooling network (RGC-ASPP-Net), by considering mass context ...
Mass segmentation is the first step in computer-aided detection (CAD) systems for classification of breast masses as malignant or benign, and it greatly ...
Multi-stream Information-Based Neural Network for Mammogram Mass Segmentation ... Segmentation of masses on mammograms using data augmentation and deep learning.
Aug 11, 2020 · Experimental results show that DUALCORENET achieves the best mammography segmentation (in both high and low resolution) and classification ...
In this paper, we extensively evaluate novel transformer-based and graph-based architectures against state-of-the-art multi-view convolutional neural networks.
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We proposed a novel mass segmentation method based on an attentive multi-task learning network (MTLNet), which is an end-to-end model to accurately segment ...
MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic ...
Multi-adversarial learning is proposed to capture multi-scale image information for accurate breast mass segmentation in mammograms by introducing the idea ...
Feb 20, 2019 · Later, Dhungel et al. proposed a multiscale deep belief network classifier, followed by a cascade of region-based convolutional neural networks ...
In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy. To resolve these issues ...