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Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders [article]

Yifei Yang, Shibing Xiang, Ruixiang Zhang
2020 arXiv   pre-print
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the  ...  For anomaly detection, we accomplish anomaly removal by replacing the original encoded image features at each scale with most relevant prototype features,and fuse these features before feeding to the decoding  ...  network for anomaly detection in retinal oct image. arXiv preprint arXiv:1911.12527 (2019) [5] Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection  ... 
arXiv:2012.11113v1 fatcat:zmt6qecypvbe3ltm7unyv2tucm

Generative Adversarial Network in Medical Imaging: A Review [article]

Xin Yi, Ekta Walia, Paul Babyn
2019 arXiv   pre-print
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.  ...  This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation.  ...  The learning process was conducted in an unsupervised fashion and effectiveness was demonstrated by state-of-the-art performance of anomaly detection on optical coherence tomography (OCT) images.  ... 
arXiv:1809.07294v3 fatcat:5j5i6shlcvbbjm74ceidzg6rc4

Table of Contents

2020 2020 IEEE International Conference on Image Processing (ICIP)  
%ULDQ 0DF1DPHH 8QLYHUVLW\ &ROOHJH 'XEOLQ ,UHODQG 6DUD 0F1DPHH 3DWULFN &XOOHQ 'HZDU )LQOD\ -DPHV 0F/DXJKOLQ 8OVWHU 8QLYHUVLW\ 8QLWHG .LQJGRP TEC-06.15: A GENERATIVE ADVERSARIAL NETWORK FOR MEDICAL IMAGE  ...  TEC-03.4: MDT: UNSUPERVISED MULTI-DOMAIN IMAGE-TO-IMAGE TRANSLATOR ......................................... 598 BASED ON GENERATIVE ADVERSARIAL NETWORKS <H /LQ .HUHQ )X 6KHQJJXL /LQJ 3HQJ &KHQJ 6LFKXDQ  ...  CODED-APERTURE FOR UNSUPERVISED CLASSIFICATION OF HYPERSEPCTRAL IMAGERY -LDQFKHQ =KX 7RQJ =KDQJ 6KHQJMLH =KDR 7RQJML 8QLYHUVLW\ &KLQD IMT-02.4: ADMM-INSPIRED RECONSTRUCTION NETWORK FOR COMPRESSIVE  ... 
doi:10.1109/icip40778.2020.9191006 fatcat:3fkxl2sjmre2jkryewwo5mlahi

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Generative Adversarial Networks with a Pair of Complementary Generators for Retinopathy Screening DAY 2 -Jan 13, 2021 Takahashi, Masami; Kohjima, Masahiro; Kurashima, Takeshi; Toda, Hiroyuki 1337  ...  Fluorescein Angiography from Retinal Fundus Images Using Generative Adversarial Networks DAY 1 -Jan 12, 2021 Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda; Crichton, Andrew 2544 Transfer Learning  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Tudor Florin Ursuleanu, Andreea Roxana Luca, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici
2021 Diagnostics  
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the  ...  completion of tasks in current applications in the interpretation of medical images.  ...  challenge (BraTS2018), Locating blood vessels in retinal images (STARE), Digital database for screening mammography (DDSM), Automated mining of large-scale lesion annotations and universal lesion detection  ... 
doi:10.3390/diagnostics11081373 fatcat:6p7usnvnxnewtivzeth745s3ga

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
Traditional approaches generally leverage the information from natural images via transfer learning.  ...  More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas  ...  ] , vessel segmentation [195] and anomaly segmentation [196] in retinal fundus image, breast mass segmentation [197] .  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

Table of Contents

2020 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)  
Using Generative Adversarial Networks, pp. 591-595.  ...  Ground Truth for Detecting Cell Types in an Image-Based Immunotherapy Screen, pp. 886-890.  ... 
doi:10.1109/isbi45749.2020.9098467 fatcat:6kxbkb2s5bdc5cmjvxjhotccay

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers.  ...  Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend.  ...  In order to perform these tasks, many deep learning paradigms were developed: convolutional neural networks (CNN), recurrent neural networks (RNN), reinforcement learning, general adversarial networks  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Sample Augmentation for Intelligent Milling Tool Wear Condition Monitoring Using Numerical Simulation and Generative Adversarial Network.  ...  ., +, TIM 2021 4500508 Telemetry Data-Based Spacecraft Anomaly Detection With Spatial-Temporal Generative Adversarial Networks.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
This shows great potential of our network to be used for unsupervised pathology detection in retinal OCT images with the advantage of generating sharply delineated segmentations.  ...  There is an adversarial training as a learning method to train Generative Adversarial Network (GAN).  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

Deep Learning Based Pain Treatment

Tarun Jaiswal, Sushma Jaiswal
2019 International Journal of Trend in Scientific Research and Development  
Indeed, the application of machine learning for pain investigationassociated non-imaging problems has been mentioned in publications in scientific journals since 1940-2018.  ...  By mining information from difficult pain-associated records and generating awareness from this, facts will be simplified.  ...  [51] GAN - - 2017 In this paper authors proposed AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly  ... 
doi:10.31142/ijtsrd23639 fatcat:tqg4u3tkgjhmjpya67g3lnewwu

Program

2021 2021 National Conference on Communications (NCC)  
The wireless transmitters and RF PA design require several new considerations to be useful for New Generation Radio Access Network (NG-RAN) in 5 G applications.  ...  In general, switch-mode PAs are a popular choice for systems where high efficiency is required. However, these PAs are inherently narrow bands.  ...  The automated retinal OCT image classification can assist the large-scale screening and the diagnosis recommendation for an ophthalmologist. research interest includes optimization for IoT networks, fog  ... 
doi:10.1109/ncc52529.2021.9530194 fatcat:ahdw5ezvtrh4nb47l2qeos3dwq

Program Book

2020 2020 5th International Conference on Universal Village (UV)  
In this paper, we implemented a Deep Convolutional Generative Adversarial Network (DCGAN) to show how to generate novel dog images from noise.  ...  Eastern Standard Time Abstract: 12B-7] Dog Image Generation using Deep Convolutional Generative Adversarial Networks Authors: Yue Zhao, Zhongkai Shangguan, Wei Fan, Zhehan Cao, Jingwen Wang Time: 21:  ...  Additional Instructions on using Microsoft Teams for First-time Users  ... 
doi:10.1109/uv50937.2020.9426196 fatcat:bikzcbilgbfp5jzssihjmswpua

Self-Organized Evolutionary Process in Sets of Interdependent Variables near the Midpoint of Phase Transition in K-Satisfiability [chapter]

Michael Korkin
2001 Lecture Notes in Computer Science  
Ackerman, John T Strategic Studies Quarterly: An Air Force-Sponsored Strategic Forum for Military, Government, and Academic Professionals. Volume 2, Number 1 -7 Acuff, Hugh F  ...  MELISSES assists in locating the critical memory accesses that are responsible for most of memory latency and are offloaded for precomputation on helper threads.  ...  DTIC Air Conditioning Equipment; Augmentation; Radiation Protection; Respirators; Surveys; Technology Assessment MATHEMATICAL AND COMPUTER SCIENCES (GENERAL) Includes general topics and overviews related  ... 
doi:10.1007/3-540-45443-8_20 fatcat:ght26tu6avh57kkp54kifofaee

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Jyotsna Talreja Wassan, Huiru Zheng, Haiying Wang
2021
Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues.  ...  retinal fundus photography, OCT, and FA/ICGA, for predicting neovascular retinal diseases. of subjects with haemorrhage in retinal fundus images modalities.  ...  In this process, DL-based methods refined retinal diagnostics by delineating fundus and OCT images.  ... 
doi:10.3390/cells10112924 pmid:34831148 pmcid:PMC8616301 fatcat:jphpbrvjdndqvkyrkuhgiuygju
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