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10.1186/s12859-018-2184-4 http://scihub22266oqcxt.onion/10.1186/s12859-018-2184-4 C5956828!5956828 !29769044     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29769044 &cmd=llinks): Failed to open stream: HTTP request failed! 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Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction |pdf=|usr=}}{{29769044 }} gab.com Text Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29769044 #FOAvip BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. RESULTS: Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. CONCLUSION: Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered. Twit Text FOAVip Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29769044 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29769044 #FOAvip English Wikipedia <ref>{{Cite pmid|29769044 }}</ref> Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction #MMPMID29769044 Faust K ; Xie Q ; Han D ; Goyle K ; Volynskaya Z ; Djuric U ; Diamandis P BMC Bioinformatics 2018[May]; 19 (1 ): 173 PMID29769044 show ga BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. RESULTS: Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. CONCLUSION: Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered. |*Neural Networks, Computer [MESH] |Artificial Intelligence/*standards [MESH] |Deep Learning/*classification [MESH] |Humans [MESH]Visualizing histopathologic deep learning classification and anomaly d(epmc).pdf DeepDyve Pubget Overpricing