2022
DOI: 10.48550/arxiv.2204.06955
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LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections

Abstract: Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task-or imaging modality specific architectures with a decisionmaking process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM… Show more

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“…This evaluation is presented in Table III, which shows results obtained by algorithms present in the literature. For CryoNuSeg, the proposed method achieved a D C value of 0.86, surpassing Mahbod et al's method [12] and showing comparable results with Hassan et al [36] and Sitnik et al [37]. Similarly, for the MoNuSeg 2018 dataset, the ensemble achieved a value higher than the methods proposed by Liao et al [38] and Li et al [39], but lower compared to the method by Hassan et al [36].…”
Section: E Evaluation Metricsmentioning
confidence: 54%
“…This evaluation is presented in Table III, which shows results obtained by algorithms present in the literature. For CryoNuSeg, the proposed method achieved a D C value of 0.86, surpassing Mahbod et al's method [12] and showing comparable results with Hassan et al [36] and Sitnik et al [37]. Similarly, for the MoNuSeg 2018 dataset, the ensemble achieved a value higher than the methods proposed by Liao et al [38] and Li et al [39], but lower compared to the method by Hassan et al [36].…”
Section: E Evaluation Metricsmentioning
confidence: 54%