Results 211 to 220 of about 1,217,238 (259)

Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning. [PDF]

Nat Commun
Moutakanni T, Bojanowski P, Chassagnon G, Hudelot C, Joulin A, LeCun Y, Muckley M, Oquab M, Revel MP, Vakalopoulou M.   +9 more
europepmc   +1 more source

Deep learning-assisted 3D CT angiography for supply-vessel localization in inflammatory peripheral pulmonary artery pseudoaneurysms. [PDF]

Front Med (Lausanne)
Li L, Li C, Wang N, Xu X, Liu D, Lin C, Lin Y, Chen Y.   +7 more
europepmc   +1 more source

Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies. [PDF]

J Thorac Dis
Xu Z, Wang X, Cao Z, Pan X, Zhang J, Li F, Chen L, Xiao Y, Liu S.   +8 more
europepmc   +1 more source

Radiomics-assisted unsupervised clustering and predictive modeling to identify optimal candidates for virtual navigation bronchoscopy localization and CT-guided localization in thoracoscopic lung resection. [PDF]

Medicine (Baltimore)
Huang Y, Xu H, Xiang F, Jin L, Chen Y, Feng W, Ma Y.   +6 more
europepmc   +1 more source

An intelligent lung cancer detection from computed tomography images using robust optimal deep transfer learning with XAI model. [PDF]

BMC Med Inform Decis Mak
Prabha B, Poonkodi M, Premalatha M.
europepmc   +1 more source

First steps in clinical implementation of a lung nodule classification method by artificial intelligence. [PDF]

Front Radiol
Ajertil A, Farahat Z, Bouallou A, Le Tertre E, Elbher MJB, El Quessar A, Kabbaj N, Cherkaoui Malki M, Chat L, Hammani L, Ngote N.   +10 more
europepmc   +1 more source

Hessian based approaches for 3D lung nodule segmentation

Expert Systems With Applications, 2016
Abstract In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer ...
Jorge Novo
exaly   +3 more sources

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On the performance of lung nodule detection, segmentation and classification

Computerized Medical Imaging and Graphics, 2021
Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from ...
Jon Xue, Guocai Liu
exaly   +3 more sources

Lung Nodule Segmentation Using Mathematical Morphology

Applied Mechanics and Materials, 2011
Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the ...
Ming Zhi Qu, Gui Rong Weng
openaire   +2 more sources

Segmentation of lung nodules based on a refined segmentation network

Medical Physics, 2023
AbstractBackgroundAccurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer.PurposeHowever, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules.
Yang, Chen, Xuewen, Hou, Yifeng, Yang, Yichen, Zhou, Yuanzhong, Xie, Shengdong, Nie   +5 more
openaire   +2 more sources