Feb 1, 2021 · In this article, we propose a fast end-to-end framework, called Fast Multi-crop Guided Attention (FMGA) network, to accurately segment lung nodules in CT ...
In addition, we give an ablation study and visualization results to illustrate how each component works for accurate lung nodule segmentation. Index Terms— ...
The proposed method simplifies conventional lung nodule malignancy suspiciousness classification by removing nodule segmentation and hand-crafted feature (e.g.,.
A fast end-to-end framework, called Fast Multi-crop Guided Attention (FMGA) network, to accurately segment lung nodules in CT images is proposed and ...
Experimental results show that FMGA achieves superior performance among the state-of-the-arts. In addition, we give an ablation study and visualization results ...
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification, Pattern Recognit, Volume 61 2017, pp.663-673. Crossref.
We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract nodule salient information by employing a novel multi-crop pooling ...
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Feb 12, 2024 · Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to ...
In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT ...
Feb 27, 2024 · The MV-CNN specialized in capturing a diverse set of nodule-sensitive features from axial, coronal and sagittal views in CT images ...