Multiscale Convolutional Neural Networks for Vision-Based Classification of Cells

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Holistically-Nested Edge Detection (2015, background): “Multi-stream learning [3, 29] A typical multi-stream learning architecture is illustrated in Fig 2(a).”
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation (2018, background): “Multi-stream architectures with inputs at different resolutions are considered in [1], while [25] proposed skipconnections to fuse feature maps derived from different layers.”
Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation (2017, background): “Other works considered multi-stream architectures, where multiple parallel networks receiving inputs at different scale are fused [4].”
DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017, methodology): “Unlike previous patch based cell classiﬁcation methods [22]–[24], [41], [42], our method does not directly operate on images containing full cells (like the images in the Herlev dataset), for practical reasons.”