{"id":26526,"date":"2024-02-26T03:24:36","date_gmt":"2024-02-26T04:24:36","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-tcupgan-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%d8%ac%d8%af%db%8c%d8%af-%d8%a7%db%8c%d8%ac%d8%a7%d8%af-%d8%b4%d8%af%d9%87-%d8%a8%d8%b1%d8%a7%db%8c-%d8%a8\/"},"modified":"2024-02-26T03:24:37","modified_gmt":"2024-02-26T04:24:37","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-tcupgan-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%d8%ac%d8%af%db%8c%d8%af-%d8%a7%db%8c%d8%ac%d8%a7%d8%af-%d8%b4%d8%af%d9%87-%d8%a8%d8%b1%d8%a7%db%8c-%d8%a8","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-tcupgan-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%d8%ac%d8%af%db%8c%d8%af-%d8%a7%db%8c%d8%ac%d8%a7%d8%af-%d8%b4%d8%af%d9%87-%d8%a8%d8%b1%d8%a7%db%8c-%d8%a8\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 TCuPGAN: \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u062c\u062f\u06cc\u062f \u0627\u06cc\u062c\u0627\u062f \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u062a\u0639\u0627\u0645\u0644 \u0627\u0646\u0633\u0627\u0646 \u0648 \u0645\u0627\u0634\u06cc\u0646 \u062f\u0631 \u062f\u0627\u0646\u0634 \u0634\u0647\u0631\u0648\u0646\u062f\u06cc"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nTCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0645\u0642\u0627\u0644\u0647 TCuPGAN: \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u062c\u062f\u06cc\u062f \u0627\u06cc\u062c\u0627\u062f \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u062a\u0639\u0627\u0645\u0644 \u0627\u0646\u0633\u0627\u0646 \u0648 \u0645\u0627\u0634\u06cc\u0646 \u062f\u0631 \u062f\u0627\u0646\u0634 \u0634\u0647\u0631\u0648\u0646\u062f\u06cc<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nRamanakumar Sankar, Kameswara Mantha, Lucy Fortson, Helen Spiers, Thomas Pengo, Douglas Mashek, Myat Mo, Mark Sanders, Trace Christensen, Jeffrey Salisbury, Laura Trouille<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\nPDF<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n6<\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nHuman-Computer Interaction,Computer Vision and Pattern Recognition,Machine Learning,\u062a\u0639\u0627\u0645\u0644 \u0627\u0646\u0633\u0627\u0646 \u0648 \u0631\u0627\u06cc\u0627\u0646\u0647 , \u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u0631\u0627\u06cc\u0627\u0646\u0647 \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0627\u0644\u06af\u0648\u06cc , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 23 November, 2023; originally announced November 2023. , Comments: 5 pages, 1 figure, accepted for publication at HLDM ’23 (ECML PKDD 2023 workshop)<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 23 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f. \u060c \u0646\u0638\u0631\u0627\u062a: 5 \u0635\u0641\u062d\u0647 \u060c 1 \u0634\u06a9\u0644 \u060c \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u0634\u0627\u0631 \u062f\u0631 HLDM ’23 \u067e\u0630\u06cc\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a (\u06a9\u0627\u0631\u06af\u0627\u0647 ECML PKDD 2023)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information. Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers. We leverage the patch-wise discriminator in our model to provide an estimate of which slices within these image cubes have poorly generalized feature representations, and correspondingly poor machine performance. These images with corresponding machine proposals would be presented to volunteers on Zooniverse for correction, leading to a drastic reduction in the volunteer effort on citizen science projects. We trained our model on ~2300 liver tissue 3D electron micrographs. Lipid droplets were segmented within these images through human annotation via the `Etch A Cell – Fat Checker’ citizen science project, hosted on the Zooniverse platform. In this work, we demonstrate this framework and the selection methodology which resulted in a measured reduction in volunteer effort by more than 60%. We envision this type of joint human-machine partnership will be of great use on future Zooniverse projects.<\/p>\n

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  • \r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t
  • \r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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