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10.1109/TMI.2020.3045295 http://scihub22266oqcxt.onion/10.1109/TMI.2020.3045295 33326377!8544938!33326377     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2021 ; 40 (3): 1032-1041 Nephropedia Template TP {{tp|p=33326377|t=2021. MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection |pdf=|usr=}}{{33326377}} gab.com Text MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33326377 #FOAvip Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net. Twit Text FOAVip MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33326377 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33326377 #FOAvip English Wikipedia <ref>{{Cite pmid|33326377}}</ref> MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection #MMPMID33326377 Chen Y; Zhang H; Wang Y; Yang Y; Zhou X; Wu QMJ IEEE Trans Med Imaging 2021[Mar]; 40 (3): 1032-1041 PMID33326377show ga Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net. |*Neural Networks, Computer[MESH] |Algorithms[MESH] |Brain/diagnostic imaging[MESH] |COVID-19/diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/diagnostic imaging[MESH] |Magnetic Resonance Imaging[MESH] |SARS-CoV-2[MESH]MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly(epmc).pdf DeepDyve Pubget Overpricing