scholarly journals SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A Novel Approach To Train Convolutional Neural Networks On Lung CT Scans Using Binary Labels Only

2019 ◽  
Author(s):  
M. Zusag ◽  
S. R. Desai ◽  
M. Di Paolo ◽  
T. Semple ◽  
A. Shah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Ct Scans ◽  
Novel Approach ◽  
Pathological Sign ◽  
Lung Ct

A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks

2019 ◽  
Vol 47 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Shaikat M. Galib ◽  
Hyoung K. Lee ◽  
Christopher L. Guy ◽  
Matthew J. Riblett ◽  
Geoffrey D. Hugo
Keyword(s):  
Neural Networks ◽  
Quality Assurance ◽  
Image Registration ◽  
Convolutional Neural Networks ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Lung Ct

scholarly journals CROSS-LANGUAGE TEXT CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS FROM SCRATCH

2017 ◽  
Vol 2 ◽  
pp. 24-33 ◽  
Author(s):  
Musbah Zaid Enweiji ◽  
Taras Lehinevych ◽  
Аndrey Glybovets
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Current Method ◽  
Classification Model ◽  
The Novel ◽  
Novel Approach ◽  
Multilingual Learning ◽  
Cross Language ◽  
Language Text ◽  
Language Classification

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

scholarly journals Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks

2019 ◽  
Vol 14 (8) ◽  
pp. 1275-1284 ◽  
Author(s):  
Farid Ouhmich ◽  
Vincent Agnus ◽  
Vincent Noblet ◽  
Fabrice Heitz ◽  
Patrick Pessaux
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Liver Tissue ◽  
Ct Scans ◽  
Tissue Segmentation ◽  
Multiphase Ct
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