scholarly journals Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning

2016 ◽  
Vol 4 ◽  
pp. 1-12 ◽  
Author(s):  
Seonyeong Park ◽  
Suk Jin Lee ◽  
Elisabeth Weiss ◽  
Yuichi Motai
Keyword(s):  
Deep Learning ◽  
Lung Tumors

scholarly journals Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

Tomography ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 358-372
Author(s):  
Matthew D. Holbrook ◽  
Darin P. Clark ◽  
Rutulkumar Patel ◽  
Yi Qi ◽  
Alex M. Bassil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Model Performance ◽  
Area Under The Curve ◽  
Simulated Data ◽  
Real Data ◽  
Lung Tumors ◽  
Training Data ◽  
Micro Ct ◽  
Micro Computed Tomography

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

scholarly journals Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm

2019 ◽  
Vol 46 (10) ◽  
pp. 4699-4707 ◽  
Author(s):  
Chuang Wang ◽  
Andreas Rimner ◽  
Yu‐Chi Hu ◽  
Neelam Tyagi ◽  
Jue Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Imaging ◽  
Learning Algorithm ◽  
Imaging Study ◽  
Lung Tumors ◽  
Deep Learning Algorithm

scholarly journals A frame work for the detection and Diagnosis of Lung Tumors using Deep learning Methods

2021 ◽  
Vol 12 (2) ◽  
pp. 2404-2409
Author(s):  
P.Jagadeesh , Et. al.
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Lung Tumors ◽  
Ct Images ◽  
Segmentation Method ◽  
Low Contrast ◽  
Frame Work ◽  
Detection And Diagnosis ◽  
Learning Architectures ◽  
Lung Ct

The detection of tumor pixels in lung images is complex task due to its low contrast property. Hence, this paper uses deep learning architectures for both the detection and diagnosis of lung tumors in Computer Tomography (CT) images. In this article, the tumors are detected in lung CT images using Convolutional Neural Networks (CNN) architecture with the help of data augmentation methods. This proposed CNN architecture classifies the lung images into two categories as tumor images and normal images. Then, the segmentation method is used to segment the tumor pixels in the lung CT images and the segmented tumor regions are classified into either mild or severe using proposed CNN architecture.

scholarly journals Computer-aided Detection of Lung Tumors in Chest X-ray Images Using a Bone Suppression Algorithm and A Deep Learning Framework

2021 ◽  
Vol 2071 (1) ◽  
pp. 012002
Author(s):  
K Sato ◽  
N Kanno ◽  
T Ishii ◽  
Y Saijo
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Support Systems ◽  
Early Stage ◽  
Learning Model ◽  
Lung Tumors ◽  
Single Shot ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Abstract Detecting lung tumors in early stage by reading chest X-ray images is important for radical treatments of the disease. In order to decrease the risk of missed lung tumors, diagnosis support systems that can provide the accurate detection of lung tumors are in high demand, and the use of artificial intelligence with deep learning is one of the promising solutions. In our research, we aim to improve the accuracy of a deep learning-based system for detecting lung tumors by developing a bone suppression algorithm as a preprocessing for the machine-learning model. Our bone suppression algorithm was devised for conventional single-shot chest X-ray images, which do not rely on a specific type of imaging systems. 604 chest X-ray images were processed using the proposed algorithm and evaluated by combining it with a U-net deep learning model. The results showed that the bone suppression algorithm successfully improved the performance of the deep learning model to identify the location of lung tumors (Intersection over Union) from 0.085 (without the bone suppression algorithm) to 0.142, as well as the ability to classify the lung cancer (Area under Curve) that increased from 0.700 to 0.736. The bone suppression algorithm would be useful to improve the accuracy and the reliability of the deep learning-based diagnosis support systems for detecting lung cancer in mass medical examinations.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Sign in / Sign up

Export Citation Format

Share Document