scholarly journals Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy

2020 ◽  
Vol 80 ◽  
pp. 101701 ◽  
Author(s):  
Joost van der Putten ◽  
Maarten Struyvenberg ◽  
Jeroen de Groof ◽  
Thom Scheeve ◽  
Wouter Curvers ◽  
...  
Keyword(s):  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Principal Dimension

scholarly journals CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images

2019 ◽  
Vol 26 (11) ◽  
pp. 1286-1296 ◽  
Author(s):  
Li Tong ◽  
Hang Wu ◽  
May D Wang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Multiclass Classification ◽  
Semisupervised Learning ◽  
Training Data ◽  
Confocal Laser Endomicroscopy ◽  
Convolutional Autoencoder

Abstract Objective This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance. Materials and Methods We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett’s esophagus at Emory Hospital, which consists of 429 labeled images and 2826 unlabeled images. Our CAESNet consists of an encoder with 5 convolutional layers, a decoder with 5 transposed convolutional layers, and a classification network with 2 fully connected layers and a softmax layer. In the unsupervised stage, we first update the encoder and decoder with both labeled and unlabeled images to learn an efficient feature representation. In the supervised stage, we further update the encoder and the classification network with only labeled images for multiclass classification of the OE images. Results Our proposed semisupervised method CAESNet achieves the best average performance for multiclass classification of OE images, which surpasses the performance of supervised methods including standard convolutional networks and convolutional autoencoder network. Conclusions Our semisupervised CAESNet can efficiently utilize the unlabeled OE images, which improves the diagnosis and decision making for patients with Barrett’s esophagus.

Assessment of ex-vivo and in-vivo near-infrared Raman spectroscopy for the classification of dysplasia within Barrett's esophagus

2000 ◽  
Author(s):  
Martin G. Shim ◽  
Louis-Michel Wong Kee Song ◽  
Norman E. Marcon ◽  
Shirley Hassaram ◽  
Brian C. Wilson
Keyword(s):  
Raman Spectroscopy ◽  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Near Infrared ◽  
Ex Vivo

Diagnostic Accuracy of Raman Spectroscopy for the Classification of Dysplastic Lesions in Barrett's Esophagus

2006 ◽  
Vol 63 (5) ◽  
pp. AB89 ◽  
Author(s):  
Louis-Michel Wong Kee Song ◽  
Andrea Molckovsky ◽  
Kenneth Wang ◽  
Navtej Buttar ◽  
Lawrence Burgart ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Diagnostic Accuracy ◽  
Barrett’S Esophagus ◽  
Barrett's Esophagus

scholarly journals Endoscopy-Driven Pretraining for Classification of Dysplasia in Barrett’s Esophagus with Endoscopic Narrow-Band Imaging Zoom Videos

2020 ◽  
Vol 10 (10) ◽  
pp. 3407 ◽  
Author(s):  
Joost van der Putten ◽  
Maarten Struyvenberg ◽  
Jeroen de Groof ◽  
Wouter Curvers ◽  
Erik Schoon ◽  
...  
Keyword(s):  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Learning System ◽  
Final Model ◽  
Step Process ◽  
First Results

Endoscopic diagnosis of early neoplasia in Barrett’s Esophagus is generally a two-step process of primary detection in overview, followed by detailed inspection of any visible abnormalities using Narrow Band Imaging (NBI). However, endoscopists struggle with evaluating NBI-zoom imagery of subtle abnormalities. In this work, we propose the first results of a deep learning system for the characterization of NBI-zoom imagery of Barrett’s Esophagus with an accuracy, sensitivity, and specificity of 83.6%, 83.1%, and 84.0%, respectively. We also show that endoscopy-driven pretraining outperforms two models, one without pretraining as well as a model with ImageNet initialization. The final model outperforms absence of pretraining by approximately 10% and the performance is 2% higher in terms of accuracy compared to ImageNet pretraining. Furthermore, the practical deployment of our model is not hampered by ImageNet licensing, thereby paving the way for clinical application.

Detection and classification of the mucosal and vascular patterns (mucosal morphology) in Barrett's esophagus by using narrow band imaging

2006 ◽  
Vol 64 (2) ◽  
pp. 155-166 ◽  
Author(s):  
Mohammed A. Kara ◽  
Mohamed Ennahachi ◽  
Paul Fockens ◽  
Fiebo J.W. ten Kate ◽  
Jacques J.G.H.M. Bergman
Keyword(s):  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Mucosal Morphology

Validation of the Prague C&M classification of Barrett's esophagus in clinical practice

Endoscopy ◽  
2013 ◽  
Vol 45 (11) ◽  
pp. 876-882 ◽  
Author(s):  
Lorenza Alvarez Herrero ◽  
Wouter Curvers ◽  
Frederike van Vilsteren ◽  
Herbert Wolfsen ◽  
Krish Ragunath ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Barrett’S Esophagus ◽  
Barrett's Esophagus

Evaluating Self-Supervised Learning Methods for Downstream Classification of Neoplasia in Barrett’s Esophagus

2021 ◽  
Author(s):  
S. Cornelissen ◽  
J.A. van der Putten ◽  
T. G. W. Boers ◽  
J.B. Jukema ◽  
K.N. Fockens ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Barrett’S Esophagus ◽  
Barrett's Esophagus ◽  
Learning Methods
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