A semisupervised knowledge distillation model for lung nodule segmentation [PDF]
Scientific ReportsEarly screening of lung nodules is mainly done manually by reading the patient’s lung CT. This approach is time-consuming laborious and prone to leakage and misdiagnosis. Current methods for lung nodule detection face limitations such as the high cost of
Wenjuan Liu +5 more
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DEHA-Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation [PDF]
Sensors, 2023 Measuring pulmonary nodules accurately can help the early diagnosis of lung cancer, which can increase the survival rate among patients. Numerous techniques for lung nodule segmentation have been developed; however, most of them either rely on the 3D ...
Muhammad Usman, Yeong-Gil Shin
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Autocorrection of lung boundary on 3D CT lung cancer images [PDF]
Journal of King Saud University: Computer and Information Sciences, 2021 Lung cancer in men has the highest mortality rate among all types of cancer. Juxta-pleural and juxta-vascular are the most common nodules located on the lung surface.
R. Nurfauzi +3 more
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Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation [PDF]
Scientific Reports, 2022 Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near ...
P. Malin Bruntha +5 more
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EGP-Net: a lung nodule segmentation network integrating edge guidance and pyramidal multi-scale contextual attention mechanisms [PDF]
Frontiers in OncologyObjectivesAccurate segmentation of pulmonary nodules in CT images is of great significance for the early screening, diagnosis, and treatment planning of lung cancer.
Xiangsuo Fan +10 more
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AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Applied Sciences, 2021 Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early.
Syeda Furruka Banu +4 more
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Lung Nodule Segmentation Algorithm With SMR-UNet
IEEE Access, 2023 Accurate segmentation of lung nodules is of great significance for the early diagnosis of lung cancer. However, due to the diverse shapes and small sizes of lung nodules, lung nodule segmentation is a difficult task. In this paper, we propose an improved
Jiachen Hou +5 more
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Lung nodule segmentation via semi-residual multi-resolution neural networks [PDF]
Open Life Sciences, 2023 The integration of deep neural networks and cloud computing has become increasingly prevalent within the domain of medical image processing, facilitated by the recent strides in neural network theory and the advent of the internet of things (IoTs).
Wang Chenyang, Dai Wei
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Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet [PDF]
Scientific ReportsThe diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods.
Dhafer Alhajim +3 more
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Lung Nodule Segmentation with a Region-Based Fast Marching Method. [PDF]
Sensors (Basel), 2021 When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust ...
Savic M, Ma Y, Ramponi G, Du W, Peng Y.
europepmc +7 more sources