Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models [PDF]
, 2023 Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities.
Paul, Manoranjan +25 more
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Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation
Cancer Imaging, 2020 Background Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge.
Xianling Dong +7 more
doaj +1 more source
lung-nodule-volume-growth-analysis.pdf
, 2022 The objective of this work was to develop an automated computerized algorithm to detect the cancerous nature of lung. An Auto Cluster K-means segmentation (ACKMS) was used to segment the lung nodules from CT scans. ACKMS algorithm was developed such that
ganesh E N (12315038)
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A quantitative evaluation of lung nodule spiculation based on image enhancement
IET Image Processing, 2023 Lung cancer manifests itself as lung nodules at an early stage. Segmentation of lung nodules and quantitative evaluation of spiculation can assist physicians in distinguishing benign and malignant lung nodules. The identification of malignant nodules for
Juliang Tao, Yongli Wang, Xiaoyun Ding
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3D Automated Lung Nodule Segmentation in HRCT [PDF]
, 2003 A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is defined in order to discriminate lung nodules from other dense (vascular) structures.
Catalin I. Fetita +3 more
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Lung Nodule Segmentation for Explainable AI-based Cancer Screening
, 2021 We present a novel approach for segmentation and identification of lung nodules in CT scans, for the purpose of Explainable AI assisted screening.
Muley, Atharva
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Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention [PDF]
, 2022 Since radiologists have different training and clinical experiences, they may provide various segmentation annotations for a lung nodule. Conventional studies choose a single annotation as the learning target by default, but they waste valuable ...
Wang, Qiuli +3 more
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Collision metastasis from prostate adenocarcinoma and pancreatic ductal adenocarcinoma to a lung nodule [PDF]
, 2023 Introduction Prostate cancer and pancreatic cancer are often complex pathologies that affect millions of patients worldwide. However, the incidence of a distant collision metastasis of both malignancies remains a rare and often poorly documented ...
Madaan, S. +4 more
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Lung nodules segmentation from CT with DeepHealth toolkit
CoRR, 2022 Workshop ICIAP 2021 - Deep-Learning and High Performance Computing to Boost Biomedical ...
Hafiza Ayesha Hoor Chaudhry +9 more
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Atrous Convolution for Binary Semantic Segmentation of Lung Nodule [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous ...
Mohammad Hesam Hesamian +3 more
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